Overview

Dataset statistics

Number of variables53
Number of observations180519
Missing cells336209
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.0 MiB
Average record size in memory424.0 B

Variable types

Categorical17
Numeric24
Text9
DateTime2
Unsupported1

Alerts

Customer Email has constant value ""Constant
Customer Password has constant value ""Constant
Product Status has constant value ""Constant
Days for shipping (real) is highly overall correlated with Days for shipment (scheduled) and 3 other fieldsHigh correlation
Benefit per order is highly overall correlated with Order Item Profit Ratio and 1 other fieldsHigh correlation
Sales per customer is highly overall correlated with Order Item Product Price and 3 other fieldsHigh correlation
Category Id is highly overall correlated with Department Id and 5 other fieldsHigh correlation
Customer Id is highly overall correlated with Order Customer Id and 1 other fieldsHigh correlation
Customer Zipcode is highly overall correlated with Latitude and 3 other fieldsHigh correlation
Department Id is highly overall correlated with Category Id and 5 other fieldsHigh correlation
Latitude is highly overall correlated with Customer Zipcode and 3 other fieldsHigh correlation
Longitude is highly overall correlated with Customer Zipcode and 3 other fieldsHigh correlation
Order Customer Id is highly overall correlated with Customer Id and 1 other fieldsHigh correlation
Order Id is highly overall correlated with Order Item Id and 2 other fieldsHigh correlation
Order Item Cardprod Id is highly overall correlated with Category Id and 5 other fieldsHigh correlation
Order Item Discount is highly overall correlated with Order Item Discount Rate and 1 other fieldsHigh correlation
Order Item Discount Rate is highly overall correlated with Order Item DiscountHigh correlation
Order Item Id is highly overall correlated with Order Id and 2 other fieldsHigh correlation
Order Item Product Price is highly overall correlated with Sales per customer and 4 other fieldsHigh correlation
Order Item Profit Ratio is highly overall correlated with Benefit per order and 1 other fieldsHigh correlation
Sales is highly overall correlated with Sales per customer and 5 other fieldsHigh correlation
Order Item Total is highly overall correlated with Sales per customer and 3 other fieldsHigh correlation
Order Profit Per Order is highly overall correlated with Benefit per order and 1 other fieldsHigh correlation
Order Zipcode is highly overall correlated with Market and 1 other fieldsHigh correlation
Product Card Id is highly overall correlated with Category Id and 5 other fieldsHigh correlation
Product Category Id is highly overall correlated with Category Id and 5 other fieldsHigh correlation
Product Price is highly overall correlated with Sales per customer and 4 other fieldsHigh correlation
Type is highly overall correlated with Order StatusHigh correlation
Days for shipment (scheduled) is highly overall correlated with Days for shipping (real) and 1 other fieldsHigh correlation
Delivery Status is highly overall correlated with Days for shipping (real) and 2 other fieldsHigh correlation
Late_delivery_risk is highly overall correlated with Days for shipping (real) and 1 other fieldsHigh correlation
Category Name is highly overall correlated with Category Id and 10 other fieldsHigh correlation
Customer Country is highly overall correlated with Customer Zipcode and 3 other fieldsHigh correlation
Customer State is highly overall correlated with Customer Zipcode and 3 other fieldsHigh correlation
Department Name is highly overall correlated with Category Id and 5 other fieldsHigh correlation
Market is highly overall correlated with Order Id and 3 other fieldsHigh correlation
Order Region is highly overall correlated with Order Id and 3 other fieldsHigh correlation
Order Status is highly overall correlated with Type and 1 other fieldsHigh correlation
Shipping Mode is highly overall correlated with Days for shipping (real) and 1 other fieldsHigh correlation
Order Zipcode has 155679 (86.2%) missing valuesMissing
Product Description has 180519 (100.0%) missing valuesMissing
Order Item Id is uniformly distributedUniform
Order Item Id has unique valuesUnique
Product Description is an unsupported type, check if it needs cleaning or further analysisUnsupported
Days for shipping (real) has 5080 (2.8%) zerosZeros
Order Item Discount has 10028 (5.6%) zerosZeros
Order Item Discount Rate has 10028 (5.6%) zerosZeros

Reproduction

Analysis started2023-07-08 12:28:53.186626
Analysis finished2023-07-08 12:34:19.816759
Duration5 minutes and 26.63 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Type
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
DEBIT
69295 
TRANSFER
49883 
PAYMENT
41725 
CASH
19616 

Length

Max length8
Median length7
Mean length6.1826068
Min length4

Characters and Unicode

Total characters1116078
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEBIT
2nd rowTRANSFER
3rd rowCASH
4th rowDEBIT
5th rowPAYMENT

Common Values

ValueCountFrequency (%)
DEBIT 69295
38.4%
TRANSFER 49883
27.6%
PAYMENT 41725
23.1%
CASH 19616
 
10.9%

Length

2023-07-08T08:34:19.942154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:20.120128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
debit 69295
38.4%
transfer 49883
27.6%
payment 41725
23.1%
cash 19616
 
10.9%

Most occurring characters

ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1116078
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1116078
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1116078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

Days for shipping (real)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.497654
Minimum0
Maximum6
Zeros5080
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:20.271197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6237218
Coefficient of variation (CV)0.46423169
Kurtosis-1.0079136
Mean3.497654
Median Absolute Deviation (MAD)1
Skewness0.084771273
Sum631393
Variance2.6364726
MonotonicityNot monotonic
2023-07-08T08:34:20.417350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 56618
31.4%
3 28765
15.9%
6 28723
15.9%
4 28513
15.8%
5 28163
15.6%
0 5080
 
2.8%
1 4657
 
2.6%
ValueCountFrequency (%)
0 5080
 
2.8%
1 4657
 
2.6%
2 56618
31.4%
3 28765
15.9%
4 28513
15.8%
5 28163
15.6%
6 28723
15.9%
ValueCountFrequency (%)
6 28723
15.9%
5 28163
15.6%
4 28513
15.8%
3 28765
15.9%
2 56618
31.4%
1 4657
 
2.6%
0 5080
 
2.8%

Days for shipment (scheduled)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
4
107752 
2
35216 
1
27814 
0
 
9737

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Length

2023-07-08T08:34:20.574987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:20.720545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring characters

ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Benefit per order
Real number (ℝ)

HIGH CORRELATION 

Distinct21998
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.974989
Minimum-4274.98
Maximum911.79999
Zeros1177
Zeros (%)0.7%
Negative33784
Negative (%)18.7%
Memory size1.4 MiB
2023-07-08T08:34:20.881663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4274.98
5-th percentile-139.251
Q17
median31.52
Q364.800003
95-th percentile132.28999
Maximum911.79999
Range5186.78
Interquartile range (IQR)57.800003

Descriptive statistics

Standard deviation104.43353
Coefficient of variation (CV)4.7523813
Kurtosis71.377259
Mean21.974989
Median Absolute Deviation (MAD)27.88
Skewness-4.7418341
Sum3966903
Variance10906.361
MonotonicityNot monotonic
2023-07-08T08:34:21.088518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1177
 
0.7%
143.9900055 199
 
0.1%
72 194
 
0.1%
46.79999924 188
 
0.1%
24 181
 
0.1%
18 175
 
0.1%
63.70000076 172
 
0.1%
62.40000153 168
 
0.1%
12 166
 
0.1%
14.39999962 166
 
0.1%
Other values (21988) 177733
98.5%
ValueCountFrequency (%)
-4274.97998 1
< 0.1%
-3442.5 1
< 0.1%
-3366 1
< 0.1%
-3000 1
< 0.1%
-2592 1
< 0.1%
-2550 1
< 0.1%
-2351.25 1
< 0.1%
-2328 1
< 0.1%
-2280 1
< 0.1%
-2255.25 1
< 0.1%
ValueCountFrequency (%)
911.7999878 1
< 0.1%
864 1
< 0.1%
721.5999756 1
< 0.1%
720.2999878 1
< 0.1%
720 2
< 0.1%
712.9500122 1
< 0.1%
708.75 1
< 0.1%
705.5999756 2
< 0.1%
705 1
< 0.1%
698.4000244 2
< 0.1%

Sales per customer
Real number (ℝ)

HIGH CORRELATION 

Distinct2927
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.10761
Minimum7.4899998
Maximum1939.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:21.326971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.4899998
5-th percentile41.5
Q1104.38
median163.99001
Q3247.39999
95-th percentile383.98001
Maximum1939.99
Range1932.5
Interquartile range (IQR)143.02

Descriptive statistics

Standard deviation120.04367
Coefficient of variation (CV)0.65559084
Kurtosis23.920362
Mean183.10761
Median Absolute Deviation (MAD)67.000008
Skewness2.8884461
Sum33054402
Variance14410.483
MonotonicityNot monotonic
2023-07-08T08:34:21.575241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.8399963 1264
 
0.7%
109.1900024 1247
 
0.7%
124.7900009 1243
 
0.7%
129.9900055 1243
 
0.7%
116.9899979 1243
 
0.7%
123.4899979 1243
 
0.7%
120.8899994 1243
 
0.7%
127.3899994 1243
 
0.7%
97.48999786 1243
 
0.7%
118.2900009 1243
 
0.7%
Other values (2917) 168064
93.1%
ValueCountFrequency (%)
7.489999771 3
 
< 0.1%
7.989999771 3
 
< 0.1%
8.18999958 3
 
< 0.1%
8.289999962 3
 
< 0.1%
8.390000343 3
 
< 0.1%
8.470000267 15
< 0.1%
8.489999771 3
 
< 0.1%
8.659999847 29
< 0.1%
8.68999958 3
 
< 0.1%
8.789999962 3
 
< 0.1%
ValueCountFrequency (%)
1939.98999 1
< 0.1%
1919.98999 1
< 0.1%
1899.98999 1
< 0.1%
1889.98999 1
< 0.1%
1859.98999 1
< 0.1%
1819.98999 1
< 0.1%
1799.98999 1
< 0.1%
1759.98999 1
< 0.1%
1739.98999 1
< 0.1%
1699.98999 1
< 0.1%

Delivery Status
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Late delivery
98977 
Advance shipping
41592 
Shipping on time
32196 
Shipping canceled
 
7754

Length

Max length17
Median length13
Mean length14.39808
Min length13

Characters and Unicode

Total characters2599127
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdvance shipping
2nd rowLate delivery
3rd rowShipping on time
4th rowAdvance shipping
5th rowAdvance shipping

Common Values

ValueCountFrequency (%)
Late delivery 98977
54.8%
Advance shipping 41592
23.0%
Shipping on time 32196
 
17.8%
Shipping canceled 7754
 
4.3%

Length

2023-07-08T08:34:21.760701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:21.916378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
late 98977
25.2%
delivery 98977
25.2%
shipping 81542
20.7%
advance 41592
10.6%
on 32196
 
8.2%
time 32196
 
8.2%
canceled 7754
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2205893
84.9%
Space Separator 212715
 
8.2%
Uppercase Letter 180519
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 386227
17.5%
i 294257
13.3%
n 163084
 
7.4%
p 163084
 
7.4%
d 148323
 
6.7%
a 148323
 
6.7%
v 140569
 
6.4%
t 131173
 
5.9%
l 106731
 
4.8%
y 98977
 
4.5%
Other values (7) 425145
19.3%
Uppercase Letter
ValueCountFrequency (%)
L 98977
54.8%
A 41592
23.0%
S 39950
22.1%
Space Separator
ValueCountFrequency (%)
212715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2386412
91.8%
Common 212715
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 386227
16.2%
i 294257
12.3%
n 163084
 
6.8%
p 163084
 
6.8%
d 148323
 
6.2%
a 148323
 
6.2%
v 140569
 
5.9%
t 131173
 
5.5%
l 106731
 
4.5%
L 98977
 
4.1%
Other values (10) 605664
25.4%
Common
ValueCountFrequency (%)
212715
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2599127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%

Late_delivery_risk
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
98977 
0
81542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Length

2023-07-08T08:34:22.070496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:22.194436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring characters

ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Category Id
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.851451
Minimum2
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:22.366124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q118
median29
Q345
95-th percentile48
Maximum76
Range74
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.640064
Coefficient of variation (CV)0.49103145
Kurtosis-0.60326101
Mean31.851451
Median Absolute Deviation (MAD)14
Skewness0.3616248
Sum5749792
Variance244.6116
MonotonicityNot monotonic
2023-07-08T08:34:22.563223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 24551
13.6%
18 22246
12.3%
24 21035
11.7%
46 19298
10.7%
45 17325
9.6%
48 15540
8.6%
43 13729
7.6%
9 12487
6.9%
29 10984
6.1%
37 2029
 
1.1%
Other values (41) 21295
11.8%
ValueCountFrequency (%)
2 138
 
0.1%
3 632
 
0.4%
4 67
 
< 0.1%
5 343
 
0.2%
6 328
 
0.2%
7 614
 
0.3%
9 12487
6.9%
10 111
 
0.1%
11 309
 
0.2%
12 423
 
0.2%
ValueCountFrequency (%)
76 650
0.4%
75 838
0.5%
74 529
0.3%
73 357
0.2%
72 492
0.3%
71 434
0.2%
70 208
 
0.1%
69 362
0.2%
68 484
0.3%
67 483
0.3%

Category Name
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Cleats
24551 
Men's Footwear
22246 
Women's Apparel
21035 
Indoor/Outdoor Games
19298 
Fishing
17325 
Other values (45)
76064 

Length

Max length20
Median length17
Mean length12.707793
Min length4

Characters and Unicode

Total characters2293998
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSporting Goods
2nd rowSporting Goods
3rd rowSporting Goods
4th rowSporting Goods
5th rowSporting Goods

Common Values

ValueCountFrequency (%)
Cleats 24551
13.6%
Men's Footwear 22246
12.3%
Women's Apparel 21035
11.7%
Indoor/Outdoor Games 19298
10.7%
Fishing 17325
9.6%
Water Sports 15540
8.6%
Camping & Hiking 13729
7.6%
Cardio Equipment 12487
6.9%
Shop By Sport 10984
6.1%
Electronics 3156
 
1.7%
Other values (40) 20168
11.2%

Length

2023-07-08T08:34:22.767394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cleats 24551
 
7.3%
men's 22737
 
6.8%
apparel 22677
 
6.8%
footwear 22246
 
6.6%
women's 21866
 
6.5%
games 20136
 
6.0%
indoor/outdoor 19298
 
5.7%
fishing 17325
 
5.2%
15613
 
4.7%
water 15540
 
4.6%
Other values (60) 133774
39.8%

Most occurring characters

ValueCountFrequency (%)
o 213963
 
9.3%
e 180726
 
7.9%
156787
 
6.8%
r 150127
 
6.5%
s 145604
 
6.3%
a 140194
 
6.1%
n 132705
 
5.8%
t 130741
 
5.7%
i 114517
 
5.0%
p 110861
 
4.8%
Other values (39) 817773
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1712275
74.6%
Uppercase Letter 342143
 
14.9%
Space Separator 156787
 
6.8%
Other Punctuation 81819
 
3.6%
Dash Punctuation 974
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 213963
12.5%
e 180726
10.6%
r 150127
8.8%
s 145604
8.5%
a 140194
8.2%
n 132705
7.8%
t 130741
7.6%
i 114517
 
6.7%
p 110861
 
6.5%
m 69683
 
4.1%
Other values (14) 323154
18.9%
Uppercase Letter
ValueCountFrequency (%)
C 56058
16.4%
S 40270
11.8%
F 39880
11.7%
W 37406
10.9%
G 27667
8.1%
A 25257
7.4%
M 24017
7.0%
I 20272
 
5.9%
O 19298
 
5.6%
E 16074
 
4.7%
Other values (9) 35944
10.5%
Other Punctuation
ValueCountFrequency (%)
' 46840
57.2%
/ 19298
23.6%
& 15613
 
19.1%
! 68
 
0.1%
Space Separator
ValueCountFrequency (%)
156787
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2054418
89.6%
Common 239580
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 213963
 
10.4%
e 180726
 
8.8%
r 150127
 
7.3%
s 145604
 
7.1%
a 140194
 
6.8%
n 132705
 
6.5%
t 130741
 
6.4%
i 114517
 
5.6%
p 110861
 
5.4%
m 69683
 
3.4%
Other values (33) 665297
32.4%
Common
ValueCountFrequency (%)
156787
65.4%
' 46840
 
19.6%
/ 19298
 
8.1%
& 15613
 
6.5%
- 974
 
0.4%
! 68
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2293998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 213963
 
9.3%
e 180726
 
7.9%
156787
 
6.8%
r 150127
 
6.5%
s 145604
 
6.3%
a 140194
 
6.1%
n 132705
 
5.8%
t 130741
 
5.7%
i 114517
 
5.0%
p 110861
 
4.8%
Other values (39) 817773
35.6%
Distinct563
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:23.065667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length7.7086235
Min length2

Characters and Unicode

Total characters1391553
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaguas
2nd rowCaguas
3rd rowSan Jose
4th rowLos Angeles
5th rowCaguas
ValueCountFrequency (%)
caguas 66770
30.8%
san 5129
 
2.4%
chicago 3937
 
1.8%
los 3417
 
1.6%
angeles 3417
 
1.6%
brooklyn 3412
 
1.6%
new 3107
 
1.4%
york 2165
 
1.0%
beach 2084
 
1.0%
city 1659
 
0.8%
Other values (585) 121822
56.2%
2023-07-08T08:34:23.523656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 228937
16.5%
s 111699
 
8.0%
u 89280
 
6.4%
g 89233
 
6.4%
o 85515
 
6.1%
C 82588
 
5.9%
e 81172
 
5.8%
n 76231
 
5.5%
l 60937
 
4.4%
i 60636
 
4.4%
Other values (42) 425325
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1138231
81.8%
Uppercase Letter 216922
 
15.6%
Space Separator 36400
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 228937
20.1%
s 111699
9.8%
u 89280
 
7.8%
g 89233
 
7.8%
o 85515
 
7.5%
e 81172
 
7.1%
n 76231
 
6.7%
l 60937
 
5.4%
i 60636
 
5.3%
r 57370
 
5.0%
Other values (16) 197221
17.3%
Uppercase Letter
ValueCountFrequency (%)
C 82588
38.1%
S 14019
 
6.5%
B 13944
 
6.4%
L 13048
 
6.0%
A 10848
 
5.0%
P 10565
 
4.9%
M 9378
 
4.3%
H 8518
 
3.9%
D 5758
 
2.7%
N 5664
 
2.6%
Other values (15) 42592
19.6%
Space Separator
ValueCountFrequency (%)
36400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1355153
97.4%
Common 36400
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 228937
16.9%
s 111699
 
8.2%
u 89280
 
6.6%
g 89233
 
6.6%
o 85515
 
6.3%
C 82588
 
6.1%
e 81172
 
6.0%
n 76231
 
5.6%
l 60937
 
4.5%
i 60636
 
4.5%
Other values (41) 388925
28.7%
Common
ValueCountFrequency (%)
36400
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1391553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 228937
16.5%
s 111699
 
8.0%
u 89280
 
6.4%
g 89233
 
6.4%
o 85515
 
6.1%
C 82588
 
5.9%
e 81172
 
5.8%
n 76231
 
5.5%
l 60937
 
4.4%
i 60636
 
4.4%
Other values (42) 425325
30.6%

Customer Country
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
EE. UU.
111146 
Puerto Rico
69373 

Length

Max length11
Median length7
Mean length8.53719
Min length7

Characters and Unicode

Total characters1541125
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPuerto Rico
2nd rowPuerto Rico
3rd rowEE. UU.
4th rowEE. UU.
5th rowPuerto Rico

Common Values

ValueCountFrequency (%)
EE. UU. 111146
61.6%
Puerto Rico 69373
38.4%

Length

2023-07-08T08:34:23.714773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:23.861667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ee 111146
30.8%
uu 111146
30.8%
puerto 69373
19.2%
rico 69373
19.2%

Most occurring characters

ValueCountFrequency (%)
E 222292
14.4%
. 222292
14.4%
U 222292
14.4%
180519
11.7%
o 138746
9.0%
P 69373
 
4.5%
u 69373
 
4.5%
e 69373
 
4.5%
r 69373
 
4.5%
t 69373
 
4.5%
Other values (3) 208119
13.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 583330
37.9%
Lowercase Letter 554984
36.0%
Other Punctuation 222292
 
14.4%
Space Separator 180519
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 138746
25.0%
u 69373
12.5%
e 69373
12.5%
r 69373
12.5%
t 69373
12.5%
i 69373
12.5%
c 69373
12.5%
Uppercase Letter
ValueCountFrequency (%)
E 222292
38.1%
U 222292
38.1%
P 69373
 
11.9%
R 69373
 
11.9%
Other Punctuation
ValueCountFrequency (%)
. 222292
100.0%
Space Separator
ValueCountFrequency (%)
180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1138314
73.9%
Common 402811
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 222292
19.5%
U 222292
19.5%
o 138746
12.2%
P 69373
 
6.1%
u 69373
 
6.1%
e 69373
 
6.1%
r 69373
 
6.1%
t 69373
 
6.1%
R 69373
 
6.1%
i 69373
 
6.1%
Common
ValueCountFrequency (%)
. 222292
55.2%
180519
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1541125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 222292
14.4%
. 222292
14.4%
U 222292
14.4%
180519
11.7%
o 138746
9.0%
P 69373
 
4.5%
u 69373
 
4.5%
e 69373
 
4.5%
r 69373
 
4.5%
t 69373
 
4.5%
Other values (3) 208119
13.5%

Customer Email
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
XXXXXXXXX
180519 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1624671
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXXXXXXXXX
2nd rowXXXXXXXXX
3rd rowXXXXXXXXX
4th rowXXXXXXXXX
5th rowXXXXXXXXX

Common Values

ValueCountFrequency (%)
XXXXXXXXX 180519
100.0%

Length

2023-07-08T08:34:24.000708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:24.129228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
xxxxxxxxx 180519
100.0%

Most occurring characters

ValueCountFrequency (%)
X 1624671
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1624671
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 1624671
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1624671
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 1624671
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1624671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
X 1624671
100.0%
Distinct782
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:24.367538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.2746581
Min length2

Characters and Unicode

Total characters952176
Distinct characters52
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)< 0.1%

Sample

1st rowCally
2nd rowIrene
3rd rowGillian
4th rowTana
5th rowOrli
ValueCountFrequency (%)
mary 65150
36.1%
james 1835
 
1.0%
robert 1759
 
1.0%
michael 1680
 
0.9%
david 1625
 
0.9%
john 1446
 
0.8%
william 1365
 
0.8%
joseph 1117
 
0.6%
jennifer 1033
 
0.6%
richard 1032
 
0.6%
Other values (771) 102477
56.8%
2023-07-08T08:34:24.794576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 153270
16.1%
r 117429
12.3%
y 88689
 
9.3%
M 74129
 
7.8%
e 72908
 
7.7%
n 54931
 
5.8%
i 46547
 
4.9%
l 35613
 
3.7%
h 34074
 
3.6%
t 29586
 
3.1%
Other values (42) 245000
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 771639
81.0%
Uppercase Letter 180537
 
19.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 153270
19.9%
r 117429
15.2%
y 88689
11.5%
e 72908
9.4%
n 54931
 
7.1%
i 46547
 
6.0%
l 35613
 
4.6%
h 34074
 
4.4%
t 29586
 
3.8%
o 28643
 
3.7%
Other values (16) 109949
14.2%
Uppercase Letter
ValueCountFrequency (%)
M 74129
41.1%
J 17904
 
9.9%
A 9925
 
5.5%
D 9002
 
5.0%
R 8843
 
4.9%
S 8325
 
4.6%
C 7590
 
4.2%
K 6357
 
3.5%
B 6086
 
3.4%
E 4836
 
2.7%
Other values (16) 27540
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 952176
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 153270
16.1%
r 117429
12.3%
y 88689
 
9.3%
M 74129
 
7.8%
e 72908
 
7.7%
n 54931
 
5.8%
i 46547
 
4.9%
l 35613
 
3.7%
h 34074
 
3.6%
t 29586
 
3.1%
Other values (42) 245000
25.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 952176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 153270
16.1%
r 117429
12.3%
y 88689
 
9.3%
M 74129
 
7.8%
e 72908
 
7.7%
n 54931
 
5.8%
i 46547
 
4.9%
l 35613
 
3.7%
h 34074
 
3.6%
t 29586
 
3.1%
Other values (42) 245000
25.7%

Customer Id
Real number (ℝ)

HIGH CORRELATION 

Distinct20652
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6691.3795
Minimum1
Maximum20757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:24.986329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile649
Q13258.5
median6457
Q39779
95-th percentile12383
Maximum20757
Range20756
Interquartile range (IQR)6520.5

Descriptive statistics

Standard deviation4162.9181
Coefficient of variation (CV)0.62213152
Kurtosis0.014898822
Mean6691.3795
Median Absolute Deviation (MAD)3263
Skewness0.48876825
Sum1.2079211 × 109
Variance17329887
MonotonicityNot monotonic
2023-07-08T08:34:25.168809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5654 47
 
< 0.1%
10591 45
 
< 0.1%
5004 45
 
< 0.1%
5715 44
 
< 0.1%
3708 44
 
< 0.1%
9371 44
 
< 0.1%
1443 43
 
< 0.1%
791 43
 
< 0.1%
12284 43
 
< 0.1%
2641 43
 
< 0.1%
Other values (20642) 180078
99.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 10
< 0.1%
3 18
< 0.1%
4 14
< 0.1%
5 7
 
< 0.1%
6 15
< 0.1%
7 22
< 0.1%
8 19
< 0.1%
9 14
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
20757 1
< 0.1%
20756 1
< 0.1%
20755 1
< 0.1%
20754 1
< 0.1%
20753 1
< 0.1%
20752 1
< 0.1%
20751 1
< 0.1%
20750 1
< 0.1%
20749 1
< 0.1%
20748 1
< 0.1%
Distinct1109
Distinct (%)0.6%
Missing8
Missing (%)< 0.1%
Memory size1.4 MiB
2023-07-08T08:34:25.444111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.7124109
Min length2

Characters and Unicode

Total characters1031153
Distinct characters51
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st rowHolloway
2nd rowLuna
3rd rowMaldonado
4th rowTate
5th rowHendricks
ValueCountFrequency (%)
smith 64104
35.5%
johnson 989
 
0.5%
brown 909
 
0.5%
williams 869
 
0.5%
jones 859
 
0.5%
garcia 724
 
0.4%
wilson 675
 
0.4%
taylor 661
 
0.4%
davis 640
 
0.4%
moore 599
 
0.3%
Other values (1099) 109482
60.7%
2023-07-08T08:34:25.877295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 98958
 
9.6%
t 89337
 
8.7%
h 80056
 
7.8%
m 76994
 
7.5%
S 72853
 
7.1%
e 71442
 
6.9%
r 61613
 
6.0%
a 60479
 
5.9%
n 60324
 
5.9%
o 54728
 
5.3%
Other values (41) 304369
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 850642
82.5%
Uppercase Letter 180511
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 98958
11.6%
t 89337
10.5%
h 80056
9.4%
m 76994
9.1%
e 71442
8.4%
r 61613
 
7.2%
a 60479
 
7.1%
n 60324
 
7.1%
o 54728
 
6.4%
l 41923
 
4.9%
Other values (16) 154788
18.2%
Uppercase Letter
ValueCountFrequency (%)
S 72853
40.4%
M 12703
 
7.0%
H 10338
 
5.7%
B 9912
 
5.5%
C 9589
 
5.3%
W 8613
 
4.8%
R 7704
 
4.3%
G 6598
 
3.7%
P 6470
 
3.6%
L 4992
 
2.8%
Other values (15) 30739
17.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1031153
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 98958
 
9.6%
t 89337
 
8.7%
h 80056
 
7.8%
m 76994
 
7.5%
S 72853
 
7.1%
e 71442
 
6.9%
r 61613
 
6.0%
a 60479
 
5.9%
n 60324
 
5.9%
o 54728
 
5.3%
Other values (41) 304369
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1031153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 98958
 
9.6%
t 89337
 
8.7%
h 80056
 
7.8%
m 76994
 
7.5%
S 72853
 
7.1%
e 71442
 
6.9%
r 61613
 
6.0%
a 60479
 
5.9%
n 60324
 
5.9%
o 54728
 
5.3%
Other values (41) 304369
29.5%

Customer Password
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
XXXXXXXXX
180519 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1624671
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXXXXXXXXX
2nd rowXXXXXXXXX
3rd rowXXXXXXXXX
4th rowXXXXXXXXX
5th rowXXXXXXXXX

Common Values

ValueCountFrequency (%)
XXXXXXXXX 180519
100.0%

Length

2023-07-08T08:34:26.045217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:26.174556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
xxxxxxxxx 180519
100.0%

Most occurring characters

ValueCountFrequency (%)
X 1624671
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1624671
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 1624671
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1624671
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 1624671
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1624671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
X 1624671
100.0%

Customer Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Consumer
93504 
Corporate
54789 
Home Office
32226 

Length

Max length11
Median length8
Mean length8.839064
Min length8

Characters and Unicode

Total characters1595619
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowConsumer
4th rowHome Office
5th rowCorporate

Common Values

ValueCountFrequency (%)
Consumer 93504
51.8%
Corporate 54789
30.4%
Home Office 32226
 
17.9%

Length

2023-07-08T08:34:26.308058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:26.449086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
consumer 93504
44.0%
corporate 54789
25.8%
home 32226
 
15.1%
office 32226
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1350648
84.6%
Uppercase Letter 212745
 
13.3%
Space Separator 32226
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 235308
17.4%
e 212745
15.8%
r 203082
15.0%
m 125730
9.3%
n 93504
 
6.9%
s 93504
 
6.9%
u 93504
 
6.9%
f 64452
 
4.8%
t 54789
 
4.1%
p 54789
 
4.1%
Other values (3) 119241
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 148293
69.7%
H 32226
 
15.1%
O 32226
 
15.1%
Space Separator
ValueCountFrequency (%)
32226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1563393
98.0%
Common 32226
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 235308
15.1%
e 212745
13.6%
r 203082
13.0%
C 148293
9.5%
m 125730
8.0%
n 93504
 
6.0%
s 93504
 
6.0%
u 93504
 
6.0%
f 64452
 
4.1%
t 54789
 
3.5%
Other values (6) 238482
15.3%
Common
ValueCountFrequency (%)
32226
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1595619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%

Customer State
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
PR
69373 
CA
29223 
NY
11327 
TX
9103 
IL
7631 
Other values (41)
53862 

Length

Max length5
Median length2
Mean length2.0000499
Min length2

Characters and Unicode

Total characters361047
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPR
2nd rowPR
3rd rowCA
4th rowCA
5th rowPR

Common Values

ValueCountFrequency (%)
PR 69373
38.4%
CA 29223
16.2%
NY 11327
 
6.3%
TX 9103
 
5.0%
IL 7631
 
4.2%
FL 5456
 
3.0%
OH 4095
 
2.3%
PA 3824
 
2.1%
MI 3804
 
2.1%
NJ 3191
 
1.8%
Other values (36) 33492
18.6%

Length

2023-07-08T08:34:26.764978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr 69373
38.4%
ca 29223
16.2%
ny 11327
 
6.3%
tx 9103
 
5.0%
il 7631
 
4.2%
fl 5456
 
3.0%
oh 4095
 
2.3%
pa 3824
 
2.1%
mi 3804
 
2.1%
nj 3191
 
1.8%
Other values (36) 33492
18.6%

Most occurring characters

ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (21) 50623
14.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 361032
> 99.9%
Decimal Number 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (14) 50608
14.0%
Decimal Number
ValueCountFrequency (%)
5 4
26.7%
9 3
20.0%
7 3
20.0%
8 2
13.3%
1 1
 
6.7%
3 1
 
6.7%
2 1
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 361032
> 99.9%
Common 15
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (14) 50608
14.0%
Common
ValueCountFrequency (%)
5 4
26.7%
9 3
20.0%
7 3
20.0%
8 2
13.3%
1 1
 
6.7%
3 1
 
6.7%
2 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (21) 50623
14.0%
Distinct7458
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:27.037591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length19.957113
Min length8

Characters and Unicode

Total characters3602638
Distinct characters61
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique483 ?
Unique (%)0.3%

Sample

1st row5365 Noble Nectar Island
2nd row2679 Rustic Loop
3rd row8510 Round Bear Gate
4th row3200 Amber Bend
5th row8671 Iron Anchor Corners
ValueCountFrequency (%)
green 4491
 
0.7%
round 4181
 
0.7%
golden 3710
 
0.6%
clear 3684
 
0.6%
rustic 3659
 
0.6%
thunder 3616
 
0.6%
honey 3604
 
0.6%
velvet 3538
 
0.6%
heather 3513
 
0.6%
misty 3508
 
0.6%
Other values (3299) 592865
94.1%
2023-07-08T08:34:27.501917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
452905
 
12.6%
e 266514
 
7.4%
a 188658
 
5.2%
n 172491
 
4.8%
r 166038
 
4.6%
o 162182
 
4.5%
l 132873
 
3.7%
i 132088
 
3.7%
t 117744
 
3.3%
s 101650
 
2.8%
Other values (51) 1709495
47.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1995175
55.4%
Decimal Number 703140
 
19.5%
Space Separator 452905
 
12.6%
Uppercase Letter 449853
 
12.5%
Dash Punctuation 1565
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 266514
13.4%
a 188658
9.5%
n 172491
 
8.6%
r 166038
 
8.3%
o 162182
 
8.1%
l 132873
 
6.7%
i 132088
 
6.6%
t 117744
 
5.9%
s 101650
 
5.1%
d 90014
 
4.5%
Other values (15) 464923
23.3%
Uppercase Letter
ValueCountFrequency (%)
C 48068
 
10.7%
B 36535
 
8.1%
H 34142
 
7.6%
R 31731
 
7.1%
P 31630
 
7.0%
S 29549
 
6.6%
L 27209
 
6.0%
G 26549
 
5.9%
M 22218
 
4.9%
F 20196
 
4.5%
Other values (14) 142026
31.6%
Decimal Number
ValueCountFrequency (%)
7 75478
10.7%
1 74531
10.6%
9 74093
10.5%
4 72421
10.3%
3 71753
10.2%
5 71310
10.1%
2 70868
10.1%
6 70687
10.1%
8 69639
9.9%
0 52360
7.4%
Space Separator
ValueCountFrequency (%)
452905
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1565
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2445028
67.9%
Common 1157610
32.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 266514
 
10.9%
a 188658
 
7.7%
n 172491
 
7.1%
r 166038
 
6.8%
o 162182
 
6.6%
l 132873
 
5.4%
i 132088
 
5.4%
t 117744
 
4.8%
s 101650
 
4.2%
d 90014
 
3.7%
Other values (39) 914776
37.4%
Common
ValueCountFrequency (%)
452905
39.1%
7 75478
 
6.5%
1 74531
 
6.4%
9 74093
 
6.4%
4 72421
 
6.3%
3 71753
 
6.2%
5 71310
 
6.2%
2 70868
 
6.1%
6 70687
 
6.1%
8 69639
 
6.0%
Other values (2) 53925
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3602638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
452905
 
12.6%
e 266514
 
7.4%
a 188658
 
5.2%
n 172491
 
4.8%
r 166038
 
4.6%
o 162182
 
4.5%
l 132873
 
3.7%
i 132088
 
3.7%
t 117744
 
3.3%
s 101650
 
2.8%
Other values (51) 1709495
47.5%

Customer Zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct995
Distinct (%)0.6%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35921.127
Minimum603
Maximum99205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:27.687011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum603
5-th percentile725
Q1725
median19380
Q378207
95-th percentile94538
Maximum99205
Range98602
Interquartile range (IQR)77482

Descriptive statistics

Standard deviation37542.461
Coefficient of variation (CV)1.045136
Kurtosis-1.4514194
Mean35921.127
Median Absolute Deviation (MAD)18655
Skewness0.49088341
Sum6.4843381 × 109
Variance1.4094364 × 109
MonotonicityNot monotonic
2023-07-08T08:34:27.913876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
725 66770
37.0%
921 337
 
0.2%
23455 334
 
0.2%
957 297
 
0.2%
79109 292
 
0.2%
33324 283
 
0.2%
80012 280
 
0.2%
33624 261
 
0.1%
92115 256
 
0.1%
92024 254
 
0.1%
Other values (985) 111152
61.6%
ValueCountFrequency (%)
603 50
 
< 0.1%
612 122
 
0.1%
674 169
 
0.1%
680 133
 
0.1%
685 126
 
0.1%
693 140
 
0.1%
698 115
 
0.1%
725 66770
37.0%
728 22
 
< 0.1%
729 48
 
< 0.1%
ValueCountFrequency (%)
99205 43
 
< 0.1%
98632 67
 
< 0.1%
98390 28
 
< 0.1%
98226 47
 
< 0.1%
98208 95
0.1%
98115 185
0.1%
98052 117
0.1%
98037 82
< 0.1%
98031 158
0.1%
98023 98
0.1%

Department Id
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4434602
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:28.077588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q37
95-th percentile7
Maximum12
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.629246
Coefficient of variation (CV)0.29930338
Kurtosis-0.18169651
Mean5.4434602
Median Absolute Deviation (MAD)1
Skewness0.27332063
Sum982648
Variance2.6544426
MonotonicityNot monotonic
2023-07-08T08:34:28.240346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 66861
37.0%
4 48998
27.1%
5 33220
18.4%
3 14525
 
8.0%
6 9686
 
5.4%
2 2479
 
1.4%
9 2026
 
1.1%
10 1465
 
0.8%
11 492
 
0.3%
8 405
 
0.2%
ValueCountFrequency (%)
2 2479
 
1.4%
3 14525
 
8.0%
4 48998
27.1%
5 33220
18.4%
6 9686
 
5.4%
7 66861
37.0%
8 405
 
0.2%
9 2026
 
1.1%
10 1465
 
0.8%
11 492
 
0.3%
ValueCountFrequency (%)
12 362
 
0.2%
11 492
 
0.3%
10 1465
 
0.8%
9 2026
 
1.1%
8 405
 
0.2%
7 66861
37.0%
6 9686
 
5.4%
5 33220
18.4%
4 48998
27.1%
3 14525
 
8.0%

Department Name
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Fan Shop
66861 
Apparel
48998 
Golf
33220 
Footwear
14525 
Outdoors
9686 
Other values (6)
7229 

Length

Max length18
Median length8
Mean length7.0397133
Min length4

Characters and Unicode

Total characters1270802
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFitness
2nd rowFitness
3rd rowFitness
4th rowFitness
5th rowFitness

Common Values

ValueCountFrequency (%)
Fan Shop 66861
37.0%
Apparel 48998
27.1%
Golf 33220
18.4%
Footwear 14525
 
8.0%
Outdoors 9686
 
5.4%
Fitness 2479
 
1.4%
Discs Shop 2026
 
1.1%
Technology 1465
 
0.8%
Pet Shop 492
 
0.3%
Book Shop 405
 
0.2%

Length

2023-07-08T08:34:28.417573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shop 69784
27.8%
fan 66861
26.6%
apparel 48998
19.5%
golf 33220
13.2%
footwear 14525
 
5.8%
outdoors 9686
 
3.9%
fitness 2479
 
1.0%
discs 2026
 
0.8%
technology 1465
 
0.6%
pet 492
 
0.2%
Other values (4) 1491
 
0.6%

Most occurring characters

ValueCountFrequency (%)
p 167780
13.2%
o 155166
12.2%
a 131470
10.3%
l 84045
 
6.6%
F 83865
 
6.6%
r 73209
 
5.8%
h 71611
 
5.6%
n 71167
 
5.6%
70870
 
5.6%
S 69784
 
5.5%
Other values (20) 291835
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 949267
74.7%
Uppercase Letter 250665
 
19.7%
Space Separator 70870
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 167780
17.7%
o 155166
16.3%
a 131470
13.8%
l 84045
8.9%
r 73209
7.7%
h 71611
7.5%
n 71167
7.5%
e 68683
7.2%
f 33220
 
3.5%
t 27906
 
2.9%
Other values (9) 65010
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
F 83865
33.5%
S 69784
27.8%
A 48998
19.5%
G 33220
 
13.3%
O 9686
 
3.9%
D 2026
 
0.8%
T 1465
 
0.6%
B 767
 
0.3%
P 492
 
0.2%
H 362
 
0.1%
Space Separator
ValueCountFrequency (%)
70870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1199932
94.4%
Common 70870
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 167780
14.0%
o 155166
12.9%
a 131470
11.0%
l 84045
 
7.0%
F 83865
 
7.0%
r 73209
 
6.1%
h 71611
 
6.0%
n 71167
 
5.9%
S 69784
 
5.8%
e 68683
 
5.7%
Other values (19) 223152
18.6%
Common
ValueCountFrequency (%)
70870
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1270802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 167780
13.2%
o 155166
12.2%
a 131470
10.3%
l 84045
 
6.6%
F 83865
 
6.6%
r 73209
 
5.8%
h 71611
 
5.6%
n 71167
 
5.6%
70870
 
5.6%
S 69784
 
5.5%
Other values (20) 291835
23.0%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct11250
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.719955
Minimum-33.937553
Maximum48.781933
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)< 0.1%
Memory size1.4 MiB
2023-07-08T08:34:28.591424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-33.937553
5-th percentile18.212931
Q118.265432
median33.144863
Q339.279617
95-th percentile42.391102
Maximum48.781933
Range82.719486
Interquartile range (IQR)21.014185

Descriptive statistics

Standard deviation9.8136463
Coefficient of variation (CV)0.33020395
Kurtosis-1.5554149
Mean29.719955
Median Absolute Deviation (MAD)8.3017807
Skewness-0.097962666
Sum5365016.5
Variance96.307654
MonotonicityNot monotonic
2023-07-08T08:34:28.777226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.2275734 417
 
0.2%
39.49591446 370
 
0.2%
18.22757721 300
 
0.2%
36.91083145 280
 
0.2%
26.0984993 270
 
0.1%
18.38011932 267
 
0.1%
33.04647064 234
 
0.1%
26.21472931 218
 
0.1%
32.75856018 218
 
0.1%
40.64759827 212
 
0.1%
Other values (11240) 177733
98.5%
ValueCountFrequency (%)
-33.93755341 9
 
< 0.1%
17.98249054 38
< 0.1%
18.00682068 22
< 0.1%
18.01836205 20
< 0.1%
18.02520371 17
< 0.1%
18.02520943 10
 
< 0.1%
18.02523232 8
 
< 0.1%
18.02528 14
 
< 0.1%
18.02529907 30
< 0.1%
18.02535057 5
 
< 0.1%
ValueCountFrequency (%)
48.78193283 6
 
< 0.1%
48.77095795 41
< 0.1%
47.92603684 7
 
< 0.1%
47.91250229 4
 
< 0.1%
47.90813828 20
 
< 0.1%
47.90808868 10
 
< 0.1%
47.88882828 61
< 0.1%
47.84322357 70
< 0.1%
47.83976364 15
 
< 0.1%
47.83055496 6
 
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct4487
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-84.915675
Minimum-158.02599
Maximum115.26308
Zeros0
Zeros (%)0.0%
Negative180414
Negative (%)99.9%
Memory size1.4 MiB
2023-07-08T08:34:28.963794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-158.02599
5-th percentile-121.43511
Q1-98.446312
median-76.847908
Q3-66.370583
95-th percentile-66.183128
Maximum115.26308
Range273.28906
Interquartile range (IQR)32.075729

Descriptive statistics

Standard deviation21.433241
Coefficient of variation (CV)-0.25240618
Kurtosis2.1809822
Mean-84.915675
Median Absolute Deviation (MAD)10.477371
Skewness-0.49846107
Sum-15328893
Variance459.38383
MonotonicityNot monotonic
2023-07-08T08:34:29.149115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-66.3706131 3821
 
2.1%
-66.37057495 3523
 
2.0%
-66.37059021 3522
 
2.0%
-66.37050629 3465
 
1.9%
-66.37055206 3417
 
1.9%
-66.37052918 3408
 
1.9%
-66.3705368 3377
 
1.9%
-66.37055969 3242
 
1.8%
-66.37060547 3230
 
1.8%
-66.37058258 3155
 
1.7%
Other values (4477) 146359
81.1%
ValueCountFrequency (%)
-158.0259857 73
< 0.1%
-158.016037 134
0.1%
-158.0102234 18
 
< 0.1%
-158.0047607 7
 
< 0.1%
-158.0046844 43
 
< 0.1%
-158.0046234 45
 
< 0.1%
-157.9980164 40
 
< 0.1%
-157.997879 46
 
< 0.1%
-157.906601 60
< 0.1%
-157.8772125 35
 
< 0.1%
ValueCountFrequency (%)
115.2630768 17
< 0.1%
115.0497894 17
< 0.1%
84.74267578 8
 
< 0.1%
82.46199799 22
< 0.1%
77.09209442 32
< 0.1%
18.57143784 9
 
< 0.1%
-12.1069355 9
 
< 0.1%
-12.13093758 33
< 0.1%
-12.13232708 11
 
< 0.1%
-12.25018311 18
< 0.1%

Market
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
LATAM
51594 
Europe
50252 
Pacific Asia
41260 
USCA
25799 
Africa
11614 

Length

Max length12
Median length6
Mean length6.7997385
Min length4

Characters and Unicode

Total characters1227482
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPacific Asia
2nd rowPacific Asia
3rd rowPacific Asia
4th rowPacific Asia
5th rowPacific Asia

Common Values

ValueCountFrequency (%)
LATAM 51594
28.6%
Europe 50252
27.8%
Pacific Asia 41260
22.9%
USCA 25799
14.3%
Africa 11614
 
6.4%

Length

2023-07-08T08:34:29.360014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:29.576061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
latam 51594
23.3%
europe 50252
22.7%
pacific 41260
18.6%
asia 41260
18.6%
usca 25799
11.6%
africa 11614
 
5.2%

Most occurring characters

ValueCountFrequency (%)
A 181861
14.8%
i 135394
 
11.0%
a 94134
 
7.7%
c 94134
 
7.7%
r 61866
 
5.0%
f 52874
 
4.3%
L 51594
 
4.2%
T 51594
 
4.2%
M 51594
 
4.2%
E 50252
 
4.1%
Other values (10) 402185
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 680670
55.5%
Uppercase Letter 505552
41.2%
Space Separator 41260
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 135394
19.9%
a 94134
13.8%
c 94134
13.8%
r 61866
9.1%
f 52874
 
7.8%
u 50252
 
7.4%
o 50252
 
7.4%
p 50252
 
7.4%
e 50252
 
7.4%
s 41260
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
A 181861
36.0%
L 51594
 
10.2%
T 51594
 
10.2%
M 51594
 
10.2%
E 50252
 
9.9%
P 41260
 
8.2%
U 25799
 
5.1%
S 25799
 
5.1%
C 25799
 
5.1%
Space Separator
ValueCountFrequency (%)
41260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1186222
96.6%
Common 41260
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 181861
15.3%
i 135394
 
11.4%
a 94134
 
7.9%
c 94134
 
7.9%
r 61866
 
5.2%
f 52874
 
4.5%
L 51594
 
4.3%
T 51594
 
4.3%
M 51594
 
4.3%
E 50252
 
4.2%
Other values (9) 360925
30.4%
Common
ValueCountFrequency (%)
41260
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1227482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 181861
14.8%
i 135394
 
11.0%
a 94134
 
7.7%
c 94134
 
7.7%
r 61866
 
5.0%
f 52874
 
4.3%
L 51594
 
4.2%
T 51594
 
4.2%
M 51594
 
4.2%
E 50252
 
4.1%
Other values (10) 402185
32.8%
Distinct3597
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:29.956060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length8.5542131
Min length2

Characters and Unicode

Total characters1544198
Distinct characters79
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st rowBekasi
2nd rowBikaner
3rd rowBikaner
4th rowTownsville
5th rowTownsville
ValueCountFrequency (%)
san 6508
 
2.9%
city 5626
 
2.5%
de 3211
 
1.4%
los 2435
 
1.1%
santo 2339
 
1.0%
new 2316
 
1.0%
york 2316
 
1.0%
domingo 2239
 
1.0%
angeles 1857
 
0.8%
tegucigalpa 1783
 
0.8%
Other values (3768) 197270
86.6%
2023-07-08T08:34:30.515348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 197677
 
12.8%
e 116504
 
7.5%
n 115087
 
7.5%
o 111297
 
7.2%
i 94427
 
6.1%
r 85703
 
5.6%
l 76707
 
5.0%
u 57916
 
3.8%
t 56384
 
3.7%
s 56123
 
3.6%
Other values (69) 576373
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1264434
81.9%
Uppercase Letter 225693
 
14.6%
Space Separator 47381
 
3.1%
Dash Punctuation 5982
 
0.4%
Other Punctuation 659
 
< 0.1%
Open Punctuation 21
 
< 0.1%
Close Punctuation 21
 
< 0.1%
Control 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 197677
15.6%
e 116504
 
9.2%
n 115087
 
9.1%
o 111297
 
8.8%
i 94427
 
7.5%
r 85703
 
6.8%
l 76707
 
6.1%
u 57916
 
4.6%
t 56384
 
4.5%
s 56123
 
4.4%
Other values (32) 296609
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 28079
12.4%
C 25625
11.4%
M 23445
 
10.4%
B 16591
 
7.4%
P 15223
 
6.7%
L 14772
 
6.5%
A 13507
 
6.0%
T 10236
 
4.5%
D 9031
 
4.0%
N 7965
 
3.5%
Other values (19) 61219
27.1%
Other Punctuation
ValueCountFrequency (%)
' 509
77.2%
? 137
 
20.8%
. 13
 
2.0%
Space Separator
ValueCountFrequency (%)
47381
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5982
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Control
ValueCountFrequency (%)
’ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1490127
96.5%
Common 54071
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 197677
 
13.3%
e 116504
 
7.8%
n 115087
 
7.7%
o 111297
 
7.5%
i 94427
 
6.3%
r 85703
 
5.8%
l 76707
 
5.1%
u 57916
 
3.9%
t 56384
 
3.8%
s 56123
 
3.8%
Other values (61) 522302
35.1%
Common
ValueCountFrequency (%)
47381
87.6%
- 5982
 
11.1%
' 509
 
0.9%
? 137
 
0.3%
( 21
 
< 0.1%
) 21
 
< 0.1%
. 13
 
< 0.1%
’ 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1528572
99.0%
None 15626
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 197677
 
12.9%
e 116504
 
7.6%
n 115087
 
7.5%
o 111297
 
7.3%
i 94427
 
6.2%
r 85703
 
5.6%
l 76707
 
5.0%
u 57916
 
3.8%
t 56384
 
3.7%
s 56123
 
3.7%
Other values (49) 560747
36.7%
None
ValueCountFrequency (%)
í 4201
26.9%
á 4180
26.8%
ó 2167
13.9%
é 1466
 
9.4%
ã 1354
 
8.7%
ú 1170
 
7.5%
ç 279
 
1.8%
ü 259
 
1.7%
ñ 166
 
1.1%
Á 148
 
0.9%
Other values (10) 236
 
1.5%
Distinct164
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:30.803942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length22
Mean length8.7728272
Min length4

Characters and Unicode

Total characters1583662
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIndonesia
2nd rowIndia
3rd rowIndia
4th rowAustralia
5th rowAustralia
ValueCountFrequency (%)
unidos 24869
 
10.8%
estados 24840
 
10.8%
francia 13222
 
5.7%
méxico 13172
 
5.7%
alemania 9564
 
4.1%
australia 8497
 
3.7%
brasil 7987
 
3.5%
reino 7302
 
3.2%
unido 7302
 
3.2%
china 5758
 
2.5%
Other values (175) 108034
46.9%
2023-07-08T08:34:31.264005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 241810
15.3%
i 166236
 
10.5%
n 119798
 
7.6%
s 117962
 
7.4%
o 109660
 
6.9%
d 83403
 
5.3%
r 66669
 
4.2%
l 62119
 
3.9%
e 53254
 
3.4%
t 50532
 
3.2%
Other values (51) 512219
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1303170
82.3%
Uppercase Letter 229618
 
14.5%
Space Separator 50028
 
3.2%
Open Punctuation 409
 
< 0.1%
Close Punctuation 409
 
< 0.1%
Dash Punctuation 28
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 241810
18.6%
i 166236
12.8%
n 119798
9.2%
s 117962
9.1%
o 109660
8.4%
d 83403
 
6.4%
r 66669
 
5.1%
l 62119
 
4.8%
e 53254
 
4.1%
t 50532
 
3.9%
Other values (22) 231727
17.8%
Uppercase Letter
ValueCountFrequency (%)
E 34032
14.8%
U 33494
14.6%
A 24433
10.6%
I 17168
7.5%
M 16328
7.1%
F 15635
6.8%
C 15397
6.7%
R 13800
 
6.0%
B 12369
 
5.4%
S 8781
 
3.8%
Other values (15) 38181
16.6%
Space Separator
ValueCountFrequency (%)
50028
100.0%
Open Punctuation
ValueCountFrequency (%)
( 409
100.0%
Close Punctuation
ValueCountFrequency (%)
) 409
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1532788
96.8%
Common 50874
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 241810
15.8%
i 166236
 
10.8%
n 119798
 
7.8%
s 117962
 
7.7%
o 109660
 
7.2%
d 83403
 
5.4%
r 66669
 
4.3%
l 62119
 
4.1%
e 53254
 
3.5%
t 50532
 
3.3%
Other values (47) 461345
30.1%
Common
ValueCountFrequency (%)
50028
98.3%
( 409
 
0.8%
) 409
 
0.8%
- 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545222
97.6%
None 38440
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 241810
15.6%
i 166236
 
10.8%
n 119798
 
7.8%
s 117962
 
7.6%
o 109660
 
7.1%
d 83403
 
5.4%
r 66669
 
4.3%
l 62119
 
4.0%
e 53254
 
3.4%
t 50532
 
3.3%
Other values (44) 473779
30.7%
None
ValueCountFrequency (%)
é 14306
37.2%
í 7138
18.6%
á 6253
16.3%
ú 6043
15.7%
ñ 3868
 
10.1%
ó 803
 
2.1%
Á 29
 
0.1%

Order Customer Id
Real number (ℝ)

HIGH CORRELATION 

Distinct20652
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6691.3795
Minimum1
Maximum20757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:31.459951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile649
Q13258.5
median6457
Q39779
95-th percentile12383
Maximum20757
Range20756
Interquartile range (IQR)6520.5

Descriptive statistics

Standard deviation4162.9181
Coefficient of variation (CV)0.62213152
Kurtosis0.014898822
Mean6691.3795
Median Absolute Deviation (MAD)3263
Skewness0.48876825
Sum1.2079211 × 109
Variance17329887
MonotonicityNot monotonic
2023-07-08T08:34:31.641972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5654 47
 
< 0.1%
10591 45
 
< 0.1%
5004 45
 
< 0.1%
5715 44
 
< 0.1%
3708 44
 
< 0.1%
9371 44
 
< 0.1%
1443 43
 
< 0.1%
791 43
 
< 0.1%
12284 43
 
< 0.1%
2641 43
 
< 0.1%
Other values (20642) 180078
99.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 10
< 0.1%
3 18
< 0.1%
4 14
< 0.1%
5 7
 
< 0.1%
6 15
< 0.1%
7 22
< 0.1%
8 19
< 0.1%
9 14
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
20757 1
< 0.1%
20756 1
< 0.1%
20755 1
< 0.1%
20754 1
< 0.1%
20753 1
< 0.1%
20752 1
< 0.1%
20751 1
< 0.1%
20750 1
< 0.1%
20749 1
< 0.1%
20748 1
< 0.1%
Distinct65752
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2015-01-01 00:00:00
Maximum2018-01-31 23:38:00
2023-07-08T08:34:31.822848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:32.021490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Order Id
Real number (ℝ)

HIGH CORRELATION 

Distinct65752
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36221.895
Minimum1
Maximum77204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:32.202966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3626.8
Q118057
median36140
Q354144
95-th percentile68596
Maximum77204
Range77203
Interquartile range (IQR)36087

Descriptive statistics

Standard deviation21045.38
Coefficient of variation (CV)0.58101266
Kurtosis-1.1529357
Mean36221.895
Median Absolute Deviation (MAD)18042
Skewness0.032708795
Sum6.5387402 × 109
Variance4.42908 × 108
MonotonicityNot monotonic
2023-07-08T08:34:32.406432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48880 5
 
< 0.1%
3605 5
 
< 0.1%
28265 5
 
< 0.1%
50037 5
 
< 0.1%
27861 5
 
< 0.1%
23412 5
 
< 0.1%
27068 5
 
< 0.1%
27717 5
 
< 0.1%
47273 5
 
< 0.1%
27914 5
 
< 0.1%
Other values (65742) 180469
> 99.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
< 0.1%
4 4
< 0.1%
5 5
< 0.1%
7 3
< 0.1%
8 4
< 0.1%
9 3
< 0.1%
10 5
< 0.1%
11 5
< 0.1%
12 5
< 0.1%
ValueCountFrequency (%)
77204 1
< 0.1%
77203 1
< 0.1%
77202 1
< 0.1%
77201 1
< 0.1%
77200 1
< 0.1%
77199 1
< 0.1%
77198 1
< 0.1%
77197 1
< 0.1%
77196 1
< 0.1%
77195 1
< 0.1%

Order Item Cardprod Id
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.50976
Minimum19
Maximum1363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:32.603953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile191
Q1403
median627
Q31004
95-th percentile1073
Maximum1363
Range1344
Interquartile range (IQR)601

Descriptive statistics

Standard deviation336.44681
Coefficient of variation (CV)0.48583692
Kurtosis-1.2674939
Mean692.50976
Median Absolute Deviation (MAD)330
Skewness0.13825461
Sum1.2501117 × 108
Variance113196.45
MonotonicityNot monotonic
2023-07-08T08:34:32.795386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 24515
13.6%
403 22246
12.3%
502 21035
11.7%
1014 19298
10.7%
1004 17325
9.6%
1073 15500
8.6%
957 13729
7.6%
191 12169
6.7%
627 10617
5.9%
1362 838
 
0.5%
Other values (108) 23247
12.9%
ValueCountFrequency (%)
19 64
 
< 0.1%
24 74
 
< 0.1%
35 65
 
< 0.1%
37 262
0.1%
44 305
0.2%
58 29
 
< 0.1%
60 10
 
< 0.1%
61 28
 
< 0.1%
78 63
 
< 0.1%
93 280
0.2%
ValueCountFrequency (%)
1363 650
0.4%
1362 838
0.5%
1361 529
0.3%
1360 357
0.2%
1359 492
0.3%
1358 434
0.2%
1357 208
 
0.1%
1356 362
0.2%
1355 484
0.3%
1354 483
0.3%

Order Item Discount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1017
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.664741
Minimum0
Maximum500
Zeros10028
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:33.114210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.4000001
median14
Q329.99
95-th percentile62.5
Maximum500
Range500
Interquartile range (IQR)24.59

Descriptive statistics

Standard deviation21.800901
Coefficient of variation (CV)1.0549806
Kurtosis25.231267
Mean20.664741
Median Absolute Deviation (MAD)10
Skewness3.0397955
Sum3730378.4
Variance475.27928
MonotonicityNot monotonic
2023-07-08T08:34:33.339364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10028
 
5.6%
6 4589
 
2.5%
12 4067
 
2.3%
4 3647
 
2.0%
8 3626
 
2.0%
10 3424
 
1.9%
36 3268
 
1.8%
30 3230
 
1.8%
20 3123
 
1.7%
9 2964
 
1.6%
Other values (1007) 138553
76.8%
ValueCountFrequency (%)
0 10028
5.6%
0.100000001 3
 
< 0.1%
0.109999999 15
 
< 0.1%
0.119999997 29
 
< 0.1%
0.150000006 7
 
< 0.1%
0.159999996 7
 
< 0.1%
0.180000007 3
 
< 0.1%
0.200000003 16
 
< 0.1%
0.219999999 6
 
< 0.1%
0.230000004 44
 
< 0.1%
ValueCountFrequency (%)
500 1
 
< 0.1%
400 1
 
< 0.1%
375 25
< 0.1%
360 1
 
< 0.1%
340 1
 
< 0.1%
320 1
 
< 0.1%
300 26
< 0.1%
270 25
< 0.1%
260 1
 
< 0.1%
255 25
< 0.1%

Order Item Discount Rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10166819
Minimum0
Maximum0.25
Zeros10028
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:33.525239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.039999999
median0.1
Q30.16
95-th percentile0.25
Maximum0.25
Range0.25
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.070415215
Coefficient of variation (CV)0.69259829
Kurtosis-0.90115686
Mean0.10166819
Median Absolute Deviation (MAD)0.059999995
Skewness0.3409276
Sum18353.04
Variance0.0049583025
MonotonicityNot monotonic
2023-07-08T08:34:33.699531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.039999999 10029
 
5.6%
0.150000006 10029
 
5.6%
0.25 10029
 
5.6%
0.200000003 10029
 
5.6%
0.180000007 10029
 
5.6%
0.170000002 10029
 
5.6%
0.050000001 10029
 
5.6%
0.159999996 10029
 
5.6%
0.129999995 10029
 
5.6%
0.119999997 10029
 
5.6%
Other values (8) 80229
44.4%
ValueCountFrequency (%)
0 10028
5.6%
0.01 10028
5.6%
0.02 10028
5.6%
0.029999999 10029
5.6%
0.039999999 10029
5.6%
0.050000001 10029
5.6%
0.059999999 10029
5.6%
0.07 10029
5.6%
0.090000004 10029
5.6%
0.100000001 10029
5.6%
ValueCountFrequency (%)
0.25 10029
5.6%
0.200000003 10029
5.6%
0.180000007 10029
5.6%
0.170000002 10029
5.6%
0.159999996 10029
5.6%
0.150000006 10029
5.6%
0.129999995 10029
5.6%
0.119999997 10029
5.6%
0.100000001 10029
5.6%
0.090000004 10029
5.6%

Order Item Id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct180519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90260
Minimum1
Maximum180519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:33.872545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9026.9
Q145130.5
median90260
Q3135389.5
95-th percentile171493.1
Maximum180519
Range180518
Interquartile range (IQR)90259

Descriptive statistics

Standard deviation52111.491
Coefficient of variation (CV)0.57734867
Kurtosis-1.2
Mean90260
Median Absolute Deviation (MAD)45130
Skewness8.4554661 × 10-18
Sum1.6293645 × 1010
Variance2.7156075 × 109
MonotonicityNot monotonic
2023-07-08T08:34:34.074364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180517 1
 
< 0.1%
42681 1
 
< 0.1%
28953 1
 
< 0.1%
39851 1
 
< 0.1%
173601 1
 
< 0.1%
46791 1
 
< 0.1%
166889 1
 
< 0.1%
31184 1
 
< 0.1%
29248 1
 
< 0.1%
161714 1
 
< 0.1%
Other values (180509) 180509
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
180519 1
< 0.1%
180518 1
< 0.1%
180517 1
< 0.1%
180516 1
< 0.1%
180515 1
< 0.1%
180514 1
< 0.1%
180513 1
< 0.1%
180512 1
< 0.1%
180511 1
< 0.1%
180510 1
< 0.1%

Order Item Product Price
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.23255
Minimum9.9899998
Maximum1999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:34.264076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile31.99
Q150
median59.990002
Q3199.99001
95-th percentile399.98001
Maximum1999.99
Range1990
Interquartile range (IQR)149.99001

Descriptive statistics

Standard deviation139.73249
Coefficient of variation (CV)0.98937881
Kurtosis23.312997
Mean141.23255
Median Absolute Deviation (MAD)39.999996
Skewness3.1910196
Sum25495159
Variance19525.169
MonotonicityNot monotonic
2023-07-08T08:34:34.462747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.99000168 24820
13.7%
129.9900055 22372
12.4%
50 21035
11.7%
49.97999954 19298
10.7%
399.980011 17325
9.6%
199.9900055 15622
8.7%
299.980011 13729
7.6%
99.98999786 12433
6.9%
39.99000168 11201
6.2%
24.98999977 2339
 
1.3%
Other values (65) 20345
11.3%
ValueCountFrequency (%)
9.989999771 285
 
0.2%
11.28999996 271
 
0.2%
11.53999996 529
 
0.3%
14.98999977 593
 
0.3%
15.98999977 602
 
0.3%
17.98999977 298
 
0.2%
19.98999977 887
 
0.5%
21.98999977 295
 
0.2%
22 308
 
0.2%
24.98999977 2339
1.3%
ValueCountFrequency (%)
1999.98999 15
 
< 0.1%
1500 442
 
0.2%
999.9899902 10
 
< 0.1%
599.9899902 21
 
< 0.1%
532.5800171 484
 
0.3%
461.480011 484
 
0.3%
452.0400085 592
 
0.3%
399.9899902 67
 
< 0.1%
399.980011 17325
9.6%
357.1000061 652
 
0.4%

Order Item Profit Ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct162
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12064664
Minimum-2.75
Maximum0.5
Zeros1177
Zeros (%)0.7%
Negative33784
Negative (%)18.7%
Memory size1.4 MiB
2023-07-08T08:34:34.655037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.75
5-th percentile-0.76999998
Q10.079999998
median0.27000001
Q30.36000001
95-th percentile0.47999999
Maximum0.5
Range3.25
Interquartile range (IQR)0.28000002

Descriptive statistics

Standard deviation0.4667956
Coefficient of variation (CV)3.8691142
Kurtosis10.157225
Mean0.12064664
Median Absolute Deviation (MAD)0.16999999
Skewness-2.8935313
Sum21779.01
Variance0.21789814
MonotonicityNot monotonic
2023-07-08T08:34:34.837885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.479999989 9197
 
5.1%
0.349999994 7997
 
4.4%
0.25999999 6577
 
3.6%
0.340000004 6507
 
3.6%
0.469999999 6378
 
3.5%
0.360000014 6108
 
3.4%
0.330000013 5789
 
3.2%
0.49000001 5688
 
3.2%
0.289999992 5478
 
3.0%
0.280000001 5403
 
3.0%
Other values (152) 115397
63.9%
ValueCountFrequency (%)
-2.75 72
 
< 0.1%
-2.700000048 252
0.1%
-2.650000095 90
 
< 0.1%
-2.599999905 181
0.1%
-2.549999952 234
0.1%
-2.5 180
0.1%
-2.450000048 18
 
< 0.1%
-2.349999905 36
 
< 0.1%
-2.299999952 18
 
< 0.1%
-2.25 36
 
< 0.1%
ValueCountFrequency (%)
0.5 2529
 
1.4%
0.49000001 5688
3.2%
0.479999989 9197
5.1%
0.469999999 6378
3.5%
0.460000008 4822
2.7%
0.449999988 3973
2.2%
0.439999998 1390
 
0.8%
0.430000007 1085
 
0.6%
0.419999987 1245
 
0.7%
0.409999996 995
 
0.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
99134 
5
20385 
3
20350 
4
20335 
2
20315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Length

2023-07-08T08:34:35.005266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:35.150611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring characters

ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Sales
Real number (ℝ)

HIGH CORRELATION 

Distinct193
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.7721
Minimum9.9899998
Maximum1999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:35.326769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile49.98
Q1119.98
median199.92
Q3299.95001
95-th percentile399.98001
Maximum1999.99
Range1990
Interquartile range (IQR)179.97001

Descriptive statistics

Standard deviation132.27308
Coefficient of variation (CV)0.64912262
Kurtosis23.936561
Mean203.7721
Median Absolute Deviation (MAD)79.939995
Skewness2.884249
Sum36784735
Variance17496.167
MonotonicityNot monotonic
2023-07-08T08:34:35.529848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.9900055 22372
 
12.4%
399.980011 17325
 
9.6%
199.9900055 15622
 
8.7%
299.980011 13729
 
7.6%
179.9700012 5016
 
2.8%
299.9500122 4988
 
2.8%
119.9800034 4968
 
2.8%
239.9600067 4955
 
2.7%
59.99000168 4893
 
2.7%
50 4432
 
2.5%
Other values (183) 82219
45.5%
ValueCountFrequency (%)
9.989999771 56
 
< 0.1%
11.28999996 271
0.2%
11.53999996 529
0.3%
14.98999977 124
 
0.1%
15.98999977 118
 
0.1%
17.98999977 62
 
< 0.1%
19.97999954 54
 
< 0.1%
19.98999977 176
 
0.1%
21.98999977 51
 
< 0.1%
22 64
 
< 0.1%
ValueCountFrequency (%)
1999.98999 15
 
< 0.1%
1500 442
 
0.2%
999.9899902 10
 
< 0.1%
599.9899902 21
 
< 0.1%
532.5800171 484
 
0.3%
500 15
 
< 0.1%
499.9500122 2510
1.4%
499.75 12
 
< 0.1%
495 14
 
< 0.1%
474.9500122 15
 
< 0.1%

Order Item Total
Real number (ℝ)

HIGH CORRELATION 

Distinct2927
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.10761
Minimum7.4899998
Maximum1939.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:35.736146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.4899998
5-th percentile41.5
Q1104.38
median163.99001
Q3247.39999
95-th percentile383.98001
Maximum1939.99
Range1932.5
Interquartile range (IQR)143.02

Descriptive statistics

Standard deviation120.04367
Coefficient of variation (CV)0.65559084
Kurtosis23.920362
Mean183.10761
Median Absolute Deviation (MAD)67.000008
Skewness2.8884461
Sum33054402
Variance14410.483
MonotonicityNot monotonic
2023-07-08T08:34:35.927445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.8399963 1264
 
0.7%
109.1900024 1247
 
0.7%
124.7900009 1243
 
0.7%
129.9900055 1243
 
0.7%
116.9899979 1243
 
0.7%
123.4899979 1243
 
0.7%
120.8899994 1243
 
0.7%
127.3899994 1243
 
0.7%
97.48999786 1243
 
0.7%
118.2900009 1243
 
0.7%
Other values (2917) 168064
93.1%
ValueCountFrequency (%)
7.489999771 3
 
< 0.1%
7.989999771 3
 
< 0.1%
8.18999958 3
 
< 0.1%
8.289999962 3
 
< 0.1%
8.390000343 3
 
< 0.1%
8.470000267 15
< 0.1%
8.489999771 3
 
< 0.1%
8.659999847 29
< 0.1%
8.68999958 3
 
< 0.1%
8.789999962 3
 
< 0.1%
ValueCountFrequency (%)
1939.98999 1
< 0.1%
1919.98999 1
< 0.1%
1899.98999 1
< 0.1%
1889.98999 1
< 0.1%
1859.98999 1
< 0.1%
1819.98999 1
< 0.1%
1799.98999 1
< 0.1%
1759.98999 1
< 0.1%
1739.98999 1
< 0.1%
1699.98999 1
< 0.1%

Order Profit Per Order
Real number (ℝ)

HIGH CORRELATION 

Distinct21998
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.974989
Minimum-4274.98
Maximum911.79999
Zeros1177
Zeros (%)0.7%
Negative33784
Negative (%)18.7%
Memory size1.4 MiB
2023-07-08T08:34:36.128816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4274.98
5-th percentile-139.251
Q17
median31.52
Q364.800003
95-th percentile132.28999
Maximum911.79999
Range5186.78
Interquartile range (IQR)57.800003

Descriptive statistics

Standard deviation104.43353
Coefficient of variation (CV)4.7523813
Kurtosis71.377259
Mean21.974989
Median Absolute Deviation (MAD)27.88
Skewness-4.7418341
Sum3966903
Variance10906.361
MonotonicityNot monotonic
2023-07-08T08:34:36.317623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1177
 
0.7%
143.9900055 199
 
0.1%
72 194
 
0.1%
46.79999924 188
 
0.1%
24 181
 
0.1%
18 175
 
0.1%
63.70000076 172
 
0.1%
62.40000153 168
 
0.1%
12 166
 
0.1%
14.39999962 166
 
0.1%
Other values (21988) 177733
98.5%
ValueCountFrequency (%)
-4274.97998 1
< 0.1%
-3442.5 1
< 0.1%
-3366 1
< 0.1%
-3000 1
< 0.1%
-2592 1
< 0.1%
-2550 1
< 0.1%
-2351.25 1
< 0.1%
-2328 1
< 0.1%
-2280 1
< 0.1%
-2255.25 1
< 0.1%
ValueCountFrequency (%)
911.7999878 1
< 0.1%
864 1
< 0.1%
721.5999756 1
< 0.1%
720.2999878 1
< 0.1%
720 2
< 0.1%
712.9500122 1
< 0.1%
708.75 1
< 0.1%
705.5999756 2
< 0.1%
705 1
< 0.1%
698.4000244 2
< 0.1%

Order Region
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Central America
28341 
Western Europe
27109 
South America
14935 
Oceania
10148 
Northern Europe
9792 
Other values (18)
90194 

Length

Max length15
Median length14
Mean length12.634304
Min length6

Characters and Unicode

Total characters2280732
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSoutheast Asia
2nd rowSouth Asia
3rd rowSouth Asia
4th rowOceania
5th rowOceania

Common Values

ValueCountFrequency (%)
Central America 28341
15.7%
Western Europe 27109
15.0%
South America 14935
 
8.3%
Oceania 10148
 
5.6%
Northern Europe 9792
 
5.4%
Southeast Asia 9539
 
5.3%
Southern Europe 9431
 
5.2%
Caribbean 8318
 
4.6%
West of USA 7993
 
4.4%
South Asia 7731
 
4.3%
Other values (13) 47182
26.1%

Length

2023-07-08T08:34:36.487029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
europe 50252
13.9%
america 43276
12.0%
asia 31112
 
8.6%
central 30571
 
8.5%
western 27109
 
7.5%
south 26711
 
7.4%
of 18953
 
5.3%
usa 18953
 
5.3%
west 17698
 
4.9%
africa 11614
 
3.2%
Other values (11) 84317
23.4%

Most occurring characters

ValueCountFrequency (%)
e 267374
 
11.7%
r 221631
 
9.7%
202017
 
8.9%
a 185888
 
8.2%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.0%
s 105425
 
4.6%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1693309
74.2%
Uppercase Letter 385406
 
16.9%
Space Separator 202017
 
8.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 267374
15.8%
r 221631
13.1%
a 185888
11.0%
t 170633
10.1%
o 129067
7.6%
n 114572
6.8%
s 105425
 
6.2%
i 104468
 
6.2%
u 97090
 
5.7%
c 65038
 
3.8%
Other values (7) 232123
13.7%
Uppercase Letter
ValueCountFrequency (%)
A 104955
27.2%
S 71678
18.6%
E 70219
18.2%
C 45735
11.9%
W 44807
11.6%
U 24840
 
6.4%
N 13024
 
3.4%
O 10148
 
2.6%
Space Separator
ValueCountFrequency (%)
202017
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2078715
91.1%
Common 202017
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 267374
12.9%
r 221631
 
10.7%
a 185888
 
8.9%
t 170633
 
8.2%
o 129067
 
6.2%
n 114572
 
5.5%
s 105425
 
5.1%
A 104955
 
5.0%
i 104468
 
5.0%
u 97090
 
4.7%
Other values (15) 577612
27.8%
Common
ValueCountFrequency (%)
202017
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2280732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 267374
 
11.7%
r 221631
 
9.7%
202017
 
8.9%
a 185888
 
8.2%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.0%
s 105425
 
4.6%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.6%
Distinct1089
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:36.679816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length10.872617
Min length3

Characters and Unicode

Total characters1962714
Distinct characters83
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowJava Occidental
2nd rowRajastán
3rd rowRajastán
4th rowQueensland
5th rowQueensland
ValueCountFrequency (%)
de 9121
 
3.5%
del 8417
 
3.2%
inglaterra 6722
 
2.5%
california 5580
 
2.1%
nueva 5473
 
2.1%
isla 4667
 
1.8%
francia 4580
 
1.7%
san 3965
 
1.5%
sur 3354
 
1.3%
norte-westfalia 3303
 
1.3%
Other values (1171) 208627
79.1%
2023-07-08T08:34:37.084232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 310562
15.8%
n 142494
 
7.3%
i 131317
 
6.7%
e 131085
 
6.7%
r 117395
 
6.0%
o 114621
 
5.8%
l 103549
 
5.3%
83290
 
4.2%
t 80118
 
4.1%
s 75596
 
3.9%
Other values (73) 672687
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1584761
80.7%
Uppercase Letter 268229
 
13.7%
Space Separator 83290
 
4.2%
Dash Punctuation 24466
 
1.2%
Open Punctuation 664
 
< 0.1%
Close Punctuation 664
 
< 0.1%
Other Punctuation 627
 
< 0.1%
Control 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 310562
19.6%
n 142494
9.0%
i 131317
8.3%
e 131085
8.3%
r 117395
 
7.4%
o 114621
 
7.2%
l 103549
 
6.5%
t 80118
 
5.1%
s 75596
 
4.8%
u 62262
 
3.9%
Other values (35) 315762
19.9%
Uppercase Letter
ValueCountFrequency (%)
C 30372
 
11.3%
S 27262
 
10.2%
A 20489
 
7.6%
P 18540
 
6.9%
M 16660
 
6.2%
N 16027
 
6.0%
I 14188
 
5.3%
B 12615
 
4.7%
G 12427
 
4.6%
L 11348
 
4.2%
Other values (20) 88301
32.9%
Other Punctuation
ValueCountFrequency (%)
? 503
80.2%
' 124
 
19.8%
Control
ValueCountFrequency (%)
Š 8
61.5%
Ž 5
38.5%
Space Separator
ValueCountFrequency (%)
83290
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24466
100.0%
Open Punctuation
ValueCountFrequency (%)
( 664
100.0%
Close Punctuation
ValueCountFrequency (%)
) 664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1852990
94.4%
Common 109724
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 310562
16.8%
n 142494
 
7.7%
i 131317
 
7.1%
e 131085
 
7.1%
r 117395
 
6.3%
o 114621
 
6.2%
l 103549
 
5.6%
t 80118
 
4.3%
s 75596
 
4.1%
u 62262
 
3.4%
Other values (65) 583991
31.5%
Common
ValueCountFrequency (%)
83290
75.9%
- 24466
 
22.3%
( 664
 
0.6%
) 664
 
0.6%
? 503
 
0.5%
' 124
 
0.1%
Š 8
 
< 0.1%
Ž 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1925211
98.1%
None 37503
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 310562
16.1%
n 142494
 
7.4%
i 131317
 
6.8%
e 131085
 
6.8%
r 117395
 
6.1%
o 114621
 
6.0%
l 103549
 
5.4%
83290
 
4.3%
t 80118
 
4.2%
s 75596
 
3.9%
Other values (48) 635184
33.0%
None
ValueCountFrequency (%)
í 11965
31.9%
á 10023
26.7%
ó 4698
 
12.5%
é 3332
 
8.9%
ñ 3173
 
8.5%
ã 2031
 
5.4%
ú 918
 
2.4%
ü 380
 
1.0%
à 171
 
0.5%
ô 168
 
0.4%
Other values (15) 644
 
1.7%

Order Status
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
COMPLETE
59491 
PENDING_PAYMENT
39832 
PROCESSING
21902 
PENDING
20227 
CLOSED
19616 
Other values (4)
19451 

Length

Max length15
Median length14
Mean length9.6239676
Min length6

Characters and Unicode

Total characters1737309
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPLETE
2nd rowPENDING
3rd rowCLOSED
4th rowCOMPLETE
5th rowPENDING_PAYMENT

Common Values

ValueCountFrequency (%)
COMPLETE 59491
33.0%
PENDING_PAYMENT 39832
22.1%
PROCESSING 21902
 
12.1%
PENDING 20227
 
11.2%
CLOSED 19616
 
10.9%
ON_HOLD 9804
 
5.4%
SUSPECTED_FRAUD 4062
 
2.3%
CANCELED 3692
 
2.0%
PAYMENT_REVIEW 1893
 
1.0%

Length

2023-07-08T08:34:37.254632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:37.428556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
complete 59491
33.0%
pending_payment 39832
22.1%
processing 21902
 
12.1%
pending 20227
 
11.2%
closed 19616
 
10.9%
on_hold 9804
 
5.4%
suspected_fraud 4062
 
2.3%
canceled 3692
 
2.0%
payment_review 1893
 
1.0%

Most occurring characters

ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1681718
96.8%
Connector Punctuation 55591
 
3.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 281578
16.7%
N 197241
11.7%
P 187239
11.1%
O 120617
7.2%
C 112455
 
6.7%
T 105278
 
6.3%
D 101295
 
6.0%
M 101216
 
6.0%
L 92603
 
5.5%
I 83854
 
5.0%
Other values (10) 298342
17.7%
Connector Punctuation
ValueCountFrequency (%)
_ 55591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1681718
96.8%
Common 55591
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 281578
16.7%
N 197241
11.7%
P 187239
11.1%
O 120617
7.2%
C 112455
 
6.7%
T 105278
 
6.3%
D 101295
 
6.0%
M 101216
 
6.0%
L 92603
 
5.5%
I 83854
 
5.0%
Other values (10) 298342
17.7%
Common
ValueCountFrequency (%)
_ 55591
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1737309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

Order Zipcode
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct609
Distinct (%)2.5%
Missing155679
Missing (%)86.2%
Infinite0
Infinite (%)0.0%
Mean55426.132
Minimum1040
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:37.765889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1040
5-th percentile10009
Q123464
median59405
Q390008
95-th percentile98026
Maximum99301
Range98261
Interquartile range (IQR)66544

Descriptive statistics

Standard deviation31919.279
Coefficient of variation (CV)0.57588862
Kurtosis-1.4817345
Mean55426.132
Median Absolute Deviation (MAD)30631
Skewness-0.1403114
Sum1.3767851 × 109
Variance1.0188404 × 109
MonotonicityNot monotonic
2023-07-08T08:34:37.940286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10035 648
 
0.4%
10009 550
 
0.3%
10024 541
 
0.3%
94122 526
 
0.3%
10011 463
 
0.3%
94110 407
 
0.2%
98105 406
 
0.2%
19140 386
 
0.2%
90049 368
 
0.2%
98103 366
 
0.2%
Other values (599) 20179
 
11.2%
(Missing) 155679
86.2%
ValueCountFrequency (%)
1040 3
 
< 0.1%
1453 17
 
< 0.1%
1752 6
 
< 0.1%
1810 13
 
< 0.1%
1841 88
< 0.1%
1852 38
< 0.1%
1915 2
 
< 0.1%
2038 39
< 0.1%
2138 14
 
< 0.1%
2148 3
 
< 0.1%
ValueCountFrequency (%)
99301 10
 
< 0.1%
99207 22
 
< 0.1%
98661 12
 
< 0.1%
98632 10
 
< 0.1%
98502 9
 
< 0.1%
98270 3
 
< 0.1%
98226 3
 
< 0.1%
98208 5
 
< 0.1%
98198 17
 
< 0.1%
98115 294
0.2%

Product Card Id
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.50976
Minimum19
Maximum1363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:38.142867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile191
Q1403
median627
Q31004
95-th percentile1073
Maximum1363
Range1344
Interquartile range (IQR)601

Descriptive statistics

Standard deviation336.44681
Coefficient of variation (CV)0.48583692
Kurtosis-1.2674939
Mean692.50976
Median Absolute Deviation (MAD)330
Skewness0.13825461
Sum1.2501117 × 108
Variance113196.45
MonotonicityNot monotonic
2023-07-08T08:34:38.346533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 24515
13.6%
403 22246
12.3%
502 21035
11.7%
1014 19298
10.7%
1004 17325
9.6%
1073 15500
8.6%
957 13729
7.6%
191 12169
6.7%
627 10617
5.9%
1362 838
 
0.5%
Other values (108) 23247
12.9%
ValueCountFrequency (%)
19 64
 
< 0.1%
24 74
 
< 0.1%
35 65
 
< 0.1%
37 262
0.1%
44 305
0.2%
58 29
 
< 0.1%
60 10
 
< 0.1%
61 28
 
< 0.1%
78 63
 
< 0.1%
93 280
0.2%
ValueCountFrequency (%)
1363 650
0.4%
1362 838
0.5%
1361 529
0.3%
1360 357
0.2%
1359 492
0.3%
1358 434
0.2%
1357 208
 
0.1%
1356 362
0.2%
1355 484
0.3%
1354 483
0.3%

Product Category Id
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.851451
Minimum2
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:38.537882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q118
median29
Q345
95-th percentile48
Maximum76
Range74
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.640064
Coefficient of variation (CV)0.49103145
Kurtosis-0.60326101
Mean31.851451
Median Absolute Deviation (MAD)14
Skewness0.3616248
Sum5749792
Variance244.6116
MonotonicityNot monotonic
2023-07-08T08:34:38.738480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 24551
13.6%
18 22246
12.3%
24 21035
11.7%
46 19298
10.7%
45 17325
9.6%
48 15540
8.6%
43 13729
7.6%
9 12487
6.9%
29 10984
6.1%
37 2029
 
1.1%
Other values (41) 21295
11.8%
ValueCountFrequency (%)
2 138
 
0.1%
3 632
 
0.4%
4 67
 
< 0.1%
5 343
 
0.2%
6 328
 
0.2%
7 614
 
0.3%
9 12487
6.9%
10 111
 
0.1%
11 309
 
0.2%
12 423
 
0.2%
ValueCountFrequency (%)
76 650
0.4%
75 838
0.5%
74 529
0.3%
73 357
0.2%
72 492
0.3%
71 434
0.2%
70 208
 
0.1%
69 362
0.2%
68 484
0.3%
67 483
0.3%

Product Description
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing180519
Missing (%)100.0%
Memory size1.4 MiB
Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:38.973820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length94
Median length90
Mean length69.947014
Min length36

Characters and Unicode

Total characters12626765
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp://images.acmesports.sports/Smart+watch
2nd rowhttp://images.acmesports.sports/Smart+watch
3rd rowhttp://images.acmesports.sports/Smart+watch
4th rowhttp://images.acmesports.sports/Smart+watch
5th rowhttp://images.acmesports.sports/Smart+watch
ValueCountFrequency (%)
http://images.acmesports.sports/perfect+fitness+perfect+rip+deck 24515
13.6%
http://images.acmesports.sports/nike+men%27s+cj+elite+2+td+football+cleat 22246
12.3%
http://images.acmesports.sports/nike+men%27s+dri-fit+victory+golf+polo 21035
11.7%
http://images.acmesports.sports/o%27brien+men%27s+neoprene+life+vest 19298
10.7%
http://images.acmesports.sports/field+%26+stream+sportsman+16+gun+fire+safe 17325
9.6%
http://images.acmesports.sports/pelican+sunstream+100+kayak 15500
8.6%
http://images.acmesports.sports/diamondback+women%27s+serene+classic+comfort+bike+2014 13729
7.6%
http://images.acmesports.sports/nike+men%27s+free+5.0%2b+running+shoe 12169
6.7%
http://images.acmesports.sports/under+armour+girls%27+toddler+spine+surge+running+shoe 10617
5.9%
http://images.acmesports.sports/fighting+video+games 838
 
0.5%
Other values (108) 23247
12.9%
2023-07-08T08:34:39.438581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 1166577
 
9.2%
e 1121625
 
8.9%
t 997512
 
7.9%
+ 913711
 
7.2%
r 703683
 
5.6%
o 666883
 
5.3%
p 620629
 
4.9%
a 593978
 
4.7%
i 559394
 
4.4%
/ 541557
 
4.3%
Other values (56) 4741216
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8804395
69.7%
Other Punctuation 1255258
 
9.9%
Uppercase Letter 1137901
 
9.0%
Math Symbol 913711
 
7.2%
Decimal Number 491276
 
3.9%
Dash Punctuation 23338
 
0.2%
Space Separator 886
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 137110
12.0%
F 120346
10.6%
P 93021
 
8.2%
D 85981
 
7.6%
M 81289
 
7.1%
N 79408
 
7.0%
C 78303
 
6.9%
T 64674
 
5.7%
G 63324
 
5.6%
B 52566
 
4.6%
Other values (15) 281879
24.8%
Lowercase Letter
ValueCountFrequency (%)
s 1166577
13.2%
e 1121625
12.7%
t 997512
11.3%
r 703683
8.0%
o 666883
7.6%
p 620629
7.0%
a 593978
 
6.7%
i 559394
 
6.4%
m 481927
 
5.5%
n 361861
 
4.1%
Other values (14) 1530326
17.4%
Decimal Number
ValueCountFrequency (%)
2 193261
39.3%
7 126306
25.7%
0 58520
 
11.9%
1 48714
 
9.9%
6 35961
 
7.3%
4 14542
 
3.0%
5 12729
 
2.6%
3 643
 
0.1%
8 555
 
0.1%
9 45
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 541557
43.1%
. 376918
30.0%
: 180519
 
14.4%
% 156264
 
12.4%
Math Symbol
ValueCountFrequency (%)
+ 913711
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23338
100.0%
Space Separator
ValueCountFrequency (%)
886
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9942296
78.7%
Common 2684469
 
21.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1166577
11.7%
e 1121625
11.3%
t 997512
 
10.0%
r 703683
 
7.1%
o 666883
 
6.7%
p 620629
 
6.2%
a 593978
 
6.0%
i 559394
 
5.6%
m 481927
 
4.8%
n 361861
 
3.6%
Other values (39) 2668227
26.8%
Common
ValueCountFrequency (%)
+ 913711
34.0%
/ 541557
20.2%
. 376918
14.0%
2 193261
 
7.2%
: 180519
 
6.7%
% 156264
 
5.8%
7 126306
 
4.7%
0 58520
 
2.2%
1 48714
 
1.8%
6 35961
 
1.3%
Other values (7) 52738
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12626765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1166577
 
9.2%
e 1121625
 
8.9%
t 997512
 
7.9%
+ 913711
 
7.2%
r 703683
 
5.6%
o 666883
 
5.3%
p 620629
 
4.9%
a 593978
 
4.7%
i 559394
 
4.4%
/ 541557
 
4.3%
Other values (56) 4741216
37.5%
Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:39.641284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length43
Mean length35.120004
Min length5

Characters and Unicode

Total characters6339828
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmart watch
2nd rowSmart watch
3rd rowSmart watch
4th rowSmart watch
5th rowSmart watch
ValueCountFrequency (%)
men's 77602
 
7.3%
nike 57309
 
5.4%
perfect 49030
 
4.6%
golf 26281
 
2.5%
rip 24515
 
2.3%
deck 24515
 
2.3%
fitness 24515
 
2.3%
cleat 22813
 
2.1%
2 22574
 
2.1%
elite 22309
 
2.1%
Other values (351) 715106
67.0%
2023-07-08T08:34:40.060630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
887824
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4011603
63.3%
Uppercase Letter 1110988
 
17.5%
Space Separator 887824
 
14.0%
Other Punctuation 157902
 
2.5%
Decimal Number 136270
 
2.1%
Dash Punctuation 23040
 
0.4%
Math Symbol 12201
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 126137
11.4%
F 119900
10.8%
P 92713
 
8.3%
D 85981
 
7.7%
M 81289
 
7.3%
N 79408
 
7.1%
C 77993
 
7.0%
T 64000
 
5.8%
G 62759
 
5.6%
R 50142
 
4.5%
Other values (15) 270666
24.4%
Lowercase Letter
ValueCountFrequency (%)
e 733504
18.3%
i 378177
9.4%
n 350567
8.7%
r 340664
8.5%
o 293733
 
7.3%
t 274503
 
6.8%
s 263028
 
6.6%
l 230952
 
5.8%
a 230847
 
5.8%
c 150579
 
3.8%
Other values (14) 765049
19.1%
Decimal Number
ValueCountFrequency (%)
0 44762
32.8%
1 34956
25.7%
2 23239
17.1%
6 18574
13.6%
5 12729
 
9.3%
4 756
 
0.6%
3 643
 
0.5%
8 555
 
0.4%
9 45
 
< 0.1%
7 11
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
' 127155
80.5%
& 17387
 
11.0%
. 12979
 
8.2%
/ 381
 
0.2%
Space Separator
ValueCountFrequency (%)
887824
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23040
100.0%
Math Symbol
ValueCountFrequency (%)
+ 12201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5122591
80.8%
Common 1217237
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 733504
 
14.3%
i 378177
 
7.4%
n 350567
 
6.8%
r 340664
 
6.7%
o 293733
 
5.7%
t 274503
 
5.4%
s 263028
 
5.1%
l 230952
 
4.5%
a 230847
 
4.5%
c 150579
 
2.9%
Other values (39) 1876037
36.6%
Common
ValueCountFrequency (%)
887824
72.9%
' 127155
 
10.4%
0 44762
 
3.7%
1 34956
 
2.9%
2 23239
 
1.9%
- 23040
 
1.9%
6 18574
 
1.5%
& 17387
 
1.4%
. 12979
 
1.1%
5 12729
 
1.0%
Other values (7) 14592
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6339828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
887824
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

Product Price
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.23255
Minimum9.9899998
Maximum1999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-07-08T08:34:40.262434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile31.99
Q150
median59.990002
Q3199.99001
95-th percentile399.98001
Maximum1999.99
Range1990
Interquartile range (IQR)149.99001

Descriptive statistics

Standard deviation139.73249
Coefficient of variation (CV)0.98937881
Kurtosis23.312997
Mean141.23255
Median Absolute Deviation (MAD)39.999996
Skewness3.1910196
Sum25495159
Variance19525.169
MonotonicityNot monotonic
2023-07-08T08:34:40.488580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.99000168 24820
13.7%
129.9900055 22372
12.4%
50 21035
11.7%
49.97999954 19298
10.7%
399.980011 17325
9.6%
199.9900055 15622
8.7%
299.980011 13729
7.6%
99.98999786 12433
6.9%
39.99000168 11201
6.2%
24.98999977 2339
 
1.3%
Other values (65) 20345
11.3%
ValueCountFrequency (%)
9.989999771 285
 
0.2%
11.28999996 271
 
0.2%
11.53999996 529
 
0.3%
14.98999977 593
 
0.3%
15.98999977 602
 
0.3%
17.98999977 298
 
0.2%
19.98999977 887
 
0.5%
21.98999977 295
 
0.2%
22 308
 
0.2%
24.98999977 2339
1.3%
ValueCountFrequency (%)
1999.98999 15
 
< 0.1%
1500 442
 
0.2%
999.9899902 10
 
< 0.1%
599.9899902 21
 
< 0.1%
532.5800171 484
 
0.3%
461.480011 484
 
0.3%
452.0400085 592
 
0.3%
399.9899902 67
 
< 0.1%
399.980011 17325
9.6%
357.1000061 652
 
0.4%

Product Status
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
180519 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 180519
100.0%

Length

2023-07-08T08:34:40.679051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:40.818552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 180519
100.0%

Most occurring characters

ValueCountFrequency (%)
0 180519
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180519
100.0%
Distinct63701
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2015-01-03 00:00:00
Maximum2018-02-06 22:14:00
2023-07-08T08:34:40.971809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:41.155294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Shipping Mode
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Standard Class
107752 
Second Class
35216 
First Class
27814 
Same Day
 
9737

Length

Max length14
Median length14
Mean length12.823969
Min length8

Characters and Unicode

Total characters2314970
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard Class
2nd rowStandard Class
3rd rowStandard Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 107752
59.7%
Second Class 35216
 
19.5%
First Class 27814
 
15.4%
Same Day 9737
 
5.4%

Length

2023-07-08T08:34:41.338491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T08:34:41.482695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
class 170782
47.3%
standard 107752
29.8%
second 35216
 
9.8%
first 27814
 
7.7%
same 9737
 
2.7%
day 9737
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1773413
76.6%
Uppercase Letter 361038
 
15.6%
Space Separator 180519
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 405760
22.9%
s 369378
20.8%
d 250720
14.1%
l 170782
9.6%
n 142968
 
8.1%
r 135566
 
7.6%
t 135566
 
7.6%
e 44953
 
2.5%
c 35216
 
2.0%
o 35216
 
2.0%
Other values (3) 47288
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 170782
47.3%
S 152705
42.3%
F 27814
 
7.7%
D 9737
 
2.7%
Space Separator
ValueCountFrequency (%)
180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2134451
92.2%
Common 180519
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 405760
19.0%
s 369378
17.3%
d 250720
11.7%
l 170782
8.0%
C 170782
8.0%
S 152705
 
7.2%
n 142968
 
6.7%
r 135566
 
6.4%
t 135566
 
6.4%
e 44953
 
2.1%
Other values (7) 155271
 
7.3%
Common
ValueCountFrequency (%)
180519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2314970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

Interactions

2023-07-08T08:34:13.616475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:53.220342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:56.812315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:00.105727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:03.673628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:07.139185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.694593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:14.051639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:17.458931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:20.930281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:24.470484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:27.740121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:31.443824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:34.924079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:38.408340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:41.877378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:45.541107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:49.097103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:52.770646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:56.231916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:59.862063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.329873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.548302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:09.933120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:13.753422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:53.360450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:56.943228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:00.243936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:03.812285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:07.322603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.833729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:14.198119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:17.594543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:21.063066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:24.606620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:27.887100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:31.576551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:35.072768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:38.541926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:42.023395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:45.686095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:49.247173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:52.908659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:56.383500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:59.997303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.452738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.687819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:10.072441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:13.893841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:53.490652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:57.078462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:00.389904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:03.949389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:07.498632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.971401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:14.325643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:17.747510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:21.199057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:24.733128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:28.032280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:31.723277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:35.202965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:38.665330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:42.165302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:45.819515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:49.379423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:53.045947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:56.527909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:00.128842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.583894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.822805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:10.228461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:14.034663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:53.642052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:57.219157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:00.537940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:04.111015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:07.669368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:11.112558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:14.471546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:17.909128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:21.356896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:24.874962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:28.186248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:31.878286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:35.349494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:38.810106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:42.322924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:45.968235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:49.538316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:53.196886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:56.685617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:00.273444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.718807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.973416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:10.375434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:14.181160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:53.796466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:57.352772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:00.684261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:04.248609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:07.806141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:11.254983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:14.611816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:18.074098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:21.529924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.013561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:28.339924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:32.025956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:35.490407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:38.980434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:42.491138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:46.125177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:49.696983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:53.354545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:56.838732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:00.415403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.831742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:07.113026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:10.520689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:14.330791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:53.924389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:57.479473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:00.813273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:04.389383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:07.926141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:11.375901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:14.752710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:18.232982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:21.674098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.140345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:28.473698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:32.159813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:35.619387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:39.125655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:42.637269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:46.272007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:49.838954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:53.482125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:56.990691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:00.559318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.953322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:07.244473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:10.647290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:14.469447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:54.050905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:57.611254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:00.958027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:04.523241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:08.199394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:11.508801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:14.889077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:18.404110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:21.812235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.262938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:28.612017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:32.292797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:35.753780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:39.252859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:42.791047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:46.409047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:49.982137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:53.619885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:57.125566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:00.698883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:04.081381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:07.386814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:10.795690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:14.616090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:54.183257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:57.747430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:01.099727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:04.667387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:08.324861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:11.644990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:15.013485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:18.601366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:21.954099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.396385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:28.752560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:32.429535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:35.883183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:39.383473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:42.928357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:46.556455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:50.113364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:53.755106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:57.261445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:00.822953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:04.210667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:07.523648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:10.929330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:14.746963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:54.313234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:57.874058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:01.244770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:04.792474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:08.467903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:11.772991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:15.133235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:18.755620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:22.075838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.526419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:28.883528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:32.561557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.005880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:39.500807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:43.063779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:46.687133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:50.244733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:53.872102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:57.390092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:00.978393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:04.341724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:07.658981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:11.056827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:14.884479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:54.453602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.005706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:01.381834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:04.928463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:08.595417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:11.914028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:15.403019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:18.909497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:22.199725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.658388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:29.039719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:32.695665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.150867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:39.651255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:43.196889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:46.821291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:50.417334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:54.024398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:57.534947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:01.147083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:04.479932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:07.802038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:11.209414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:15.014686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:54.575680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.133214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:01.641994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:05.057690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:08.741717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:12.051640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:15.518680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.040102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:22.332742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.786350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:29.198832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:32.837610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.283207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:39.807452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:43.327693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:46.950473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:50.576152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:54.163819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:57.804591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:01.340120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:04.751415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:07.930540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:11.337574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:15.167200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:54.811979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.277242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:01.785083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:05.202282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:08.917471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:12.200635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:15.665690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.195173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:22.490835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:25.925045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:29.380032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:33.027169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.440532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:39.994762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:43.476184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:47.112207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:50.746717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:54.324970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:57.951781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:01.516148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:04.864428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:08.075844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:11.615667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:15.303768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:54.962362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.427145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:01.931794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:05.343227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:09.052386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:12.349989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:15.798890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.332187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:22.631937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:26.065216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:29.564941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:33.165636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.573374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:40.167582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:43.625369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:47.264919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:50.903327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:54.471579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:58.094685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:01.668296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:04.988589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:08.219232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:11.783744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:15.450398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:55.097214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.563116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:02.071249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:05.477024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:09.186168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:12.490647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:15.919119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.455611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:22.784142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:26.204511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:29.701160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:33.295055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.712108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:40.296822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:43.768156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:47.409565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:51.039214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:54.621009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:58.239806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:01.798986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:05.137671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:08.357595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:11.957910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:15.594790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:55.230996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.692307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:02.205415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:05.609583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:09.314462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:12.617934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:16.057180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.587428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:22.913755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:26.337431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:29.849255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:33.442331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.861089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:40.429527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:43.904022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:47.552227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:51.305090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:54.753400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:58.378068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:01.930691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:05.257324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:08.505170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:12.119850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:15.736789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:55.378453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.830439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:02.349637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:05.748850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:09.458122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:12.755543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:16.196570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.737442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:23.185787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:26.472958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:29.990265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:33.594195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:36.992819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:40.573781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:44.078420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:47.696757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:51.444076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:54.911611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:58.535102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:02.065858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:05.375861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:08.649405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:12.283353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:15.902850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:55.529814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:58.972981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:02.507380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:05.900546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:09.596533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:12.925507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:16.338851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.865899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:23.334575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:26.623637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:30.273352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:33.739291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:37.259442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:40.719837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:44.220215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:47.873550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:51.587731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:55.060914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:58.691233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:02.224828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:05.509288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:08.792546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:12.441531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:16.046456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:55.674636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:59.142292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:02.648504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:06.058849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:09.735678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:13.075113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:16.479156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:19.993797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:23.474560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:26.753963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:30.422252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:33.878744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:37.397797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:40.859913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:44.488869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:48.016978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:51.726806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:55.210468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:58.841647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:02.362068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:05.634709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:08.929804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:12.578183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:16.201380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:55.814406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:59.277615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:02.801490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:06.214028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:09.880423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:13.218073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:16.636592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:20.138626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:23.617333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:26.903977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:30.570118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:34.033952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:37.543132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:41.016531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:44.638002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:48.175081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:51.892972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:55.364401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:58.987603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:02.503716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:05.758203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:09.078039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:12.724620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:16.362227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:55.973459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:59.419857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:02.954717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:06.359870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.017085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:13.359763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:16.784148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:20.276892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:23.780132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:27.040484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:30.732877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:34.186483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:37.685133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:41.161400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:44.800152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:48.319401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:52.045214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:55.511065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:59.140823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:02.647061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:05.877141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:09.227010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:12.880717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:16.492751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:56.114792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:59.544562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:03.092630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:06.488459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.135455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:13.477141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:16.911415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:20.401639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:23.906278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:27.165273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:30.863697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:34.313244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:37.801001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:41.293936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:44.942347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:48.473842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:52.192245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:55.643647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:59.265918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:02.772501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.013124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:09.354376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:13.034791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:16.626797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:56.296645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:59.679690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:03.223588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:06.626668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.275204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:13.609290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:17.040407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:20.534390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:24.042427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:27.317266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:30.986033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:34.443836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:37.943412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:41.425481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:45.074775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:48.608313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:52.325055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:55.770414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:59.392515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:02.901111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.139887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:09.480922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:13.161592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:16.762391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:56.474119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:59.827137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:03.362054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:06.771363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.415434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:13.758307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:17.175987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:20.662085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:24.181562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:27.456354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:31.137943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:34.599827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:38.094994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:41.576205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:45.241382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:48.773106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:52.480798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:55.916251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:59.549325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.042455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.276634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:09.633958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:13.323469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:16.930165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:56.638439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:32:59.962216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:03.518936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:06.939302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:10.549645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:13.908430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:17.308408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:20.791573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:24.316291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:27.605108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:31.297230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:34.745499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:38.252254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:41.725260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:45.391518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:48.929713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:52.623903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:56.079531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:33:59.705778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:03.195277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:06.410561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:09.789239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-08T08:34:13.463223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-08T08:34:41.652970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Days for shipping (real)Benefit per orderSales per customerCategory IdCustomer IdCustomer ZipcodeDepartment IdLatitudeLongitudeOrder Customer IdOrder IdOrder Item Cardprod IdOrder Item DiscountOrder Item Discount RateOrder Item IdOrder Item Product PriceOrder Item Profit RatioSalesOrder Item TotalOrder Profit Per OrderOrder ZipcodeProduct Card IdProduct Category IdProduct PriceTypeDays for shipment (scheduled)Delivery StatusLate_delivery_riskCategory NameCustomer CountryCustomer SegmentCustomer StateDepartment NameMarketOrder Item QuantityOrder RegionOrder StatusShipping Mode
Days for shipping (real)1.000-0.003-0.000-0.0020.004-0.002-0.002-0.0030.0010.004-0.002-0.0020.0010.001-0.002-0.000-0.003-0.000-0.000-0.003-0.002-0.002-0.002-0.0000.0100.6810.5610.6310.0060.0110.0090.0270.0060.0080.0000.0220.0080.681
Benefit per order-0.0031.0000.4410.0610.0030.0020.0850.002-0.0030.0030.0170.0600.196-0.0540.0170.2870.8080.4360.4411.000-0.0070.0600.0610.2870.0030.0020.0000.0000.2320.0000.0000.0110.1270.0210.0340.0140.0000.002
Sales per customer-0.0000.4411.0000.1620.013-0.0010.2240.0000.0030.0130.0410.1600.420-0.1330.0410.639-0.0000.9861.0000.441-0.0030.1600.1620.6390.0050.0040.0050.0000.4800.0000.0060.0000.2410.0360.2120.0260.0060.004
Category Id-0.0020.0610.1621.0000.1330.0020.9080.0020.0150.1330.1350.9980.0730.0000.1350.195-0.0020.1780.1620.061-0.0070.9981.0000.1950.0010.0090.0000.0000.9730.0000.0080.0080.6830.1580.2320.1180.0010.009
Customer Id0.0040.0030.0130.1331.000-0.0000.0610.006-0.0111.0000.1330.1330.0030.0020.1330.039-0.0000.0140.0130.003-0.0060.1330.1330.0390.0050.0090.0060.0040.5120.0150.0310.0500.3360.1790.0990.1340.0080.009
Customer Zipcode-0.0020.002-0.0010.002-0.0001.0000.0030.639-0.946-0.000-0.0020.002-0.001-0.001-0.0020.0020.005-0.001-0.0010.0020.0110.0020.0020.0020.0100.0130.0120.0120.0020.9020.0280.9740.0020.0130.0000.0180.0100.013
Department Id-0.0020.0850.2240.9080.0610.0031.0000.0030.0040.0610.0630.9050.1020.0000.0630.202-0.0030.2410.2240.085-0.0060.9050.9080.2020.0000.0100.0010.0000.9930.0000.0060.0101.0000.1190.2400.0890.0010.010
Latitude-0.0030.0020.0000.0020.0060.6390.0031.000-0.6150.006-0.0020.002-0.003-0.004-0.0020.0010.002-0.0000.0000.002-0.0080.0020.0020.0010.0030.0080.0090.0070.0000.9790.0200.7990.0000.0080.0010.0150.0110.008
Longitude0.001-0.0030.0030.015-0.011-0.9460.004-0.6151.000-0.0110.0210.0150.0010.0000.0210.004-0.0050.0030.003-0.003-0.0110.0150.0150.0040.0100.0100.0120.0100.0000.8060.0230.6440.0000.0130.0000.0180.0110.010
Order Customer Id0.0040.0030.0130.1331.000-0.0000.0610.006-0.0111.0000.1330.1330.0030.0020.1330.039-0.0000.0140.0130.003-0.0060.1330.1330.0390.0050.0090.0060.0040.5120.0150.0310.0500.3360.1790.0990.1340.0080.009
Order Id-0.0020.0170.0410.1350.133-0.0020.063-0.0020.0210.1331.0000.1310.0190.0011.0000.0820.0010.0440.0410.0170.0140.1310.1350.0820.0150.0100.0090.0060.3370.0120.0060.0260.2420.7530.0980.5510.0120.010
Order Item Cardprod Id-0.0020.0600.1600.9980.1330.0020.9050.0020.0150.1330.1311.0000.0720.0010.1310.192-0.0020.1760.1600.060-0.0071.0000.9980.1920.0000.0000.0000.0000.9840.0000.0000.0080.7840.1290.2420.1050.0000.000
Order Item Discount0.0010.1960.4200.0730.003-0.0010.102-0.0030.0010.0030.0190.0721.0000.7950.0190.356-0.0020.5340.4200.1960.0000.0720.0730.3560.0000.0000.0000.0000.3020.0010.0020.0050.1620.0240.0820.0180.0000.000
Order Item Discount Rate0.001-0.054-0.1330.0000.002-0.0010.000-0.0040.0000.0020.0010.0010.7951.0000.0010.000-0.0020.000-0.133-0.0540.0000.0010.0000.0000.0000.0000.0040.0060.0000.0030.0000.0000.0000.0000.0000.0000.0020.000
Order Item Id-0.0020.0170.0410.1350.133-0.0020.063-0.0020.0210.1331.0000.1310.0190.0011.0000.0820.0010.0440.0410.0170.0140.1310.1350.0820.0150.0100.0080.0080.2320.0110.0070.0250.1660.7140.0760.5320.0110.010
Order Item Product Price-0.0000.2870.6390.1950.0390.0020.2020.0010.0040.0390.0820.1920.3560.0000.0821.0000.0000.6670.6390.287-0.0010.1920.1951.0000.0050.0080.0000.0040.8070.0000.0020.0000.3940.0590.2290.0600.0030.008
Order Item Profit Ratio-0.0030.808-0.000-0.002-0.0000.005-0.0030.002-0.005-0.0000.001-0.002-0.002-0.0020.0010.0001.000-0.001-0.0000.808-0.006-0.002-0.0020.0000.0000.0000.0000.0000.0020.0000.0000.0010.0000.0050.0000.0000.0000.000
Sales-0.0000.4360.9860.1780.014-0.0010.241-0.0000.0030.0140.0440.1760.5340.0000.0440.667-0.0011.0000.9860.436-0.0030.1760.1780.6670.0050.0060.0000.0020.6850.0000.0040.0000.3430.0420.2350.0420.0040.006
Order Item Total-0.0000.4411.0000.1620.013-0.0010.2240.0000.0030.0130.0410.1600.420-0.1330.0410.639-0.0000.9861.0000.441-0.0030.1600.1620.6390.0050.0040.0050.0000.4800.0000.0060.0000.2410.0360.2120.0260.0060.004
Order Profit Per Order-0.0031.0000.4410.0610.0030.0020.0850.002-0.0030.0030.0170.0600.196-0.0540.0170.2870.8080.4360.4411.000-0.0070.0600.0610.2870.0030.0020.0000.0000.2320.0000.0000.0110.1270.0210.0340.0140.0000.002
Order Zipcode-0.002-0.007-0.003-0.007-0.0060.011-0.006-0.008-0.011-0.0060.014-0.0070.0000.0000.014-0.001-0.006-0.003-0.003-0.0071.000-0.007-0.007-0.0010.0320.0390.0250.0310.0000.0340.0240.0720.0071.0000.0000.9220.0310.039
Product Card Id-0.0020.0600.1600.9980.1330.0020.9050.0020.0150.1330.1311.0000.0720.0010.1310.192-0.0020.1760.1600.060-0.0071.0000.9980.1920.0000.0000.0000.0000.9840.0000.0000.0080.7840.1290.2420.1050.0000.000
Product Category Id-0.0020.0610.1621.0000.1330.0020.9080.0020.0150.1330.1350.9980.0730.0000.1350.195-0.0020.1780.1620.061-0.0070.9981.0000.1950.0010.0090.0000.0000.9730.0000.0080.0080.6830.1580.2320.1180.0010.009
Product Price-0.0000.2870.6390.1950.0390.0020.2020.0010.0040.0390.0820.1920.3560.0000.0821.0000.0000.6670.6390.287-0.0010.1920.1951.0000.0050.0080.0000.0040.8070.0000.0020.0000.3940.0590.2290.0600.0030.008
Type0.0100.0030.0050.0010.0050.0100.0000.0030.0100.0050.0150.0000.0000.0000.0150.0050.0000.0050.0050.0030.0320.0000.0010.0051.0000.0060.1980.0780.0000.0060.0050.0230.0000.0110.0000.0191.0000.006
Days for shipment (scheduled)0.6810.0020.0040.0090.0090.0130.0100.0080.0100.0090.0100.0000.0000.0000.0100.0080.0000.0060.0040.0020.0390.0000.0090.0080.0061.0000.3210.4570.0180.0090.0080.0270.0100.0070.0020.0230.0091.000
Delivery Status0.5610.0000.0050.0000.0060.0120.0010.0090.0120.0060.0090.0000.0000.0040.0080.0000.0000.0000.0050.0000.0250.0000.0000.0000.1980.3211.0001.0000.0000.0030.0050.0240.0000.0060.0000.0180.5770.321
Late_delivery_risk0.6310.0000.0000.0000.0040.0120.0000.0070.0100.0040.0060.0000.0000.0060.0080.0040.0000.0020.0000.0000.0310.0000.0000.0040.0780.4571.0001.0000.0000.0000.0000.0180.0000.0050.0000.0170.2340.457
Category Name0.0060.2320.4800.9730.5120.0020.9930.0000.0000.5120.3370.9840.3020.0000.2320.8070.0020.6850.4800.2320.0000.9840.9730.8070.0000.0180.0000.0001.0000.0000.0150.0080.9940.1760.3940.0840.0020.018
Customer Country0.0110.0000.0000.0000.0150.9020.0000.9790.8060.0150.0120.0000.0010.0030.0110.0000.0000.0000.0000.0000.0340.0000.0000.0000.0060.0090.0030.0000.0001.0000.0151.0000.0000.0150.0000.0200.0090.009
Customer Segment0.0090.0000.0060.0080.0310.0280.0060.0200.0230.0310.0060.0000.0020.0000.0070.0020.0000.0040.0060.0000.0240.0000.0080.0020.0050.0080.0050.0000.0150.0151.0000.0610.0060.0020.0040.0170.0110.008
Customer State0.0270.0110.0000.0080.0500.9740.0100.7990.6440.0500.0260.0080.0050.0000.0250.0000.0010.0000.0000.0110.0720.0080.0080.0000.0230.0270.0240.0180.0081.0000.0611.0000.0090.0300.0000.0260.0220.027
Department Name0.0060.1270.2410.6830.3360.0021.0000.0000.0000.3360.2420.7840.1620.0000.1660.3940.0000.3430.2410.1270.0070.7840.6830.3940.0000.0100.0000.0000.9940.0000.0060.0091.0000.1200.2410.0850.0000.010
Market0.0080.0210.0360.1580.1790.0130.1190.0080.0130.1790.7530.1290.0240.0000.7140.0590.0050.0420.0360.0211.0000.1290.1580.0590.0110.0070.0060.0050.1760.0150.0020.0300.1201.0000.0271.0000.0110.007
Order Item Quantity0.0000.0340.2120.2320.0990.0000.2400.0010.0000.0990.0980.2420.0820.0000.0760.2290.0000.2350.2120.0340.0000.2420.2320.2290.0000.0020.0000.0000.3940.0000.0040.0000.2410.0271.0000.0310.0000.002
Order Region0.0220.0140.0260.1180.1340.0180.0890.0150.0180.1340.5510.1050.0180.0000.5320.0600.0000.0420.0260.0140.9220.1050.1180.0600.0190.0230.0180.0170.0840.0200.0170.0260.0851.0000.0311.0000.0170.023
Order Status0.0080.0000.0060.0010.0080.0100.0010.0110.0110.0080.0120.0000.0000.0020.0110.0030.0000.0040.0060.0000.0310.0000.0010.0031.0000.0090.5770.2340.0020.0090.0110.0220.0000.0110.0000.0171.0000.009
Shipping Mode0.6810.0020.0040.0090.0090.0130.0100.0080.0100.0090.0100.0000.0000.0000.0100.0080.0000.0060.0040.0020.0390.0000.0090.0080.0061.0000.3210.4570.0180.0090.0080.0270.0100.0070.0020.0230.0091.000

Missing values

2023-07-08T08:34:17.446015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T08:34:18.274940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T08:34:19.378401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TypeDays for shipping (real)Days for shipment (scheduled)Benefit per orderSales per customerDelivery StatusLate_delivery_riskCategory IdCategory NameCustomer CityCustomer CountryCustomer EmailCustomer FnameCustomer IdCustomer LnameCustomer PasswordCustomer SegmentCustomer StateCustomer StreetCustomer ZipcodeDepartment IdDepartment NameLatitudeLongitudeMarketOrder CityOrder CountryOrder Customer Idorder date (DateOrders)Order IdOrder Item Cardprod IdOrder Item DiscountOrder Item Discount RateOrder Item IdOrder Item Product PriceOrder Item Profit RatioOrder Item QuantitySalesOrder Item TotalOrder Profit Per OrderOrder RegionOrder StateOrder StatusOrder ZipcodeProduct Card IdProduct Category IdProduct DescriptionProduct ImageProduct NameProduct PriceProduct Statusshipping date (DateOrders)Shipping Mode
0DEBIT3491.250000314.640015Advance shipping073Sporting GoodsCaguasPuerto RicoXXXXXXXXXCally20755HollowayXXXXXXXXXConsumerPR5365 Noble Nectar Island725.02Fitness18.251453-66.037056Pacific AsiaBekasiIndonesia207551/31/2018 22:5677202136013.1100000.04180517327.750.291327.75314.64001591.250000Southeast AsiaJava OccidentalCOMPLETENaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7502/3/2018 22:56Standard Class
1TRANSFER54-249.089996311.359985Late delivery173Sporting GoodsCaguasPuerto RicoXXXXXXXXXIrene19492LunaXXXXXXXXXConsumerPR2679 Rustic Loop725.02Fitness18.279451-66.037064Pacific AsiaBikanerIndia194921/13/2018 12:2775939136016.3899990.05179254327.75-0.801327.75311.359985-249.089996South AsiaRajastánPENDINGNaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/18/2018 12:27Standard Class
2CASH44-247.779999309.720001Shipping on time073Sporting GoodsSan JoseEE. UU.XXXXXXXXXGillian19491MaldonadoXXXXXXXXXConsumerCA8510 Round Bear Gate95125.02Fitness37.292233-121.881279Pacific AsiaBikanerIndia194911/13/2018 12:0675938136018.0300010.06179253327.75-0.801327.75309.720001-247.779999South AsiaRajastánCLOSEDNaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/17/2018 12:06Standard Class
3DEBIT3422.860001304.809998Advance shipping073Sporting GoodsLos AngelesEE. UU.XXXXXXXXXTana19490TateXXXXXXXXXHome OfficeCA3200 Amber Bend90027.02Fitness34.125946-118.291016Pacific AsiaTownsvilleAustralia194901/13/2018 11:4575937136022.9400010.07179252327.750.081327.75304.80999822.860001OceaniaQueenslandCOMPLETENaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/16/2018 11:45Standard Class
4PAYMENT24134.210007298.250000Advance shipping073Sporting GoodsCaguasPuerto RicoXXXXXXXXXOrli19489HendricksXXXXXXXXXCorporatePR8671 Iron Anchor Corners725.02Fitness18.253769-66.037048Pacific AsiaTownsvilleAustralia194891/13/2018 11:2475936136029.5000000.09179251327.750.451327.75298.250000134.210007OceaniaQueenslandPENDING_PAYMENTNaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/15/2018 11:24Standard Class
5TRANSFER6418.580000294.980011Shipping canceled073Sporting GoodsTonawandaEE. UU.XXXXXXXXXKimberly19488FlowersXXXXXXXXXConsumerNY2122 Hazy Corner14150.02Fitness43.013969-78.879066Pacific AsiaToowoombaAustralia194881/13/2018 11:0375935136032.7799990.10179250327.750.061327.75294.98001118.580000OceaniaQueenslandCANCELEDNaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/19/2018 11:03Standard Class
6DEBIT2195.180000288.420013Late delivery173Sporting GoodsCaguasPuerto RicoXXXXXXXXXConstance19487TerrellXXXXXXXXXHome OfficePR1879 Green Pine Bank725.02Fitness18.242538-66.037056Pacific AsiaGuangzhouChina194871/13/2018 10:4275934136039.3300020.12179249327.750.331327.75288.42001395.180000Eastern AsiaGuangdongCOMPLETENaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/15/2018 10:42First Class
7TRANSFER2168.430000285.140015Late delivery173Sporting GoodsMiamiEE. UU.XXXXXXXXXErica19486StevensXXXXXXXXXCorporateFL7595 Cotton Log Row33162.02Fitness25.928869-80.162872Pacific AsiaGuangzhouChina194861/13/2018 10:2175933136042.6100010.13179248327.750.241327.75285.14001568.430000Eastern AsiaGuangdongPROCESSINGNaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/15/2018 10:21First Class
8CASH32133.720001278.589996Late delivery173Sporting GoodsCaguasPuerto RicoXXXXXXXXXNichole19485OlsenXXXXXXXXXCorporatePR2051 Dusty Route725.02Fitness18.233223-66.037056Pacific AsiaGuangzhouChina194851/13/2018 10:0075932136049.1600000.15179247327.750.481327.75278.589996133.720001Eastern AsiaGuangdongCLOSEDNaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/16/2018 10:00Second Class
9CASH21132.149994275.309998Late delivery173Sporting GoodsSan RamonEE. UU.XXXXXXXXXOprah19484DelacruzXXXXXXXXXCorporateCA9139 Blue Blossom Court94583.02Fitness37.773991-121.966629Pacific AsiaGuangzhouChina194841/13/2018 9:3975931136052.4399990.16179246327.750.481327.75275.309998132.149994Eastern AsiaGuangdongCLOSEDNaN136073NaNhttp://images.acmesports.sports/Smart+watchSmart watch327.7501/15/2018 9:39First Class
TypeDays for shipping (real)Days for shipment (scheduled)Benefit per orderSales per customerDelivery StatusLate_delivery_riskCategory IdCategory NameCustomer CityCustomer CountryCustomer EmailCustomer FnameCustomer IdCustomer LnameCustomer PasswordCustomer SegmentCustomer StateCustomer StreetCustomer ZipcodeDepartment IdDepartment NameLatitudeLongitudeMarketOrder CityOrder CountryOrder Customer Idorder date (DateOrders)Order IdOrder Item Cardprod IdOrder Item DiscountOrder Item Discount RateOrder Item IdOrder Item Product PriceOrder Item Profit RatioOrder Item QuantitySalesOrder Item TotalOrder Profit Per OrderOrder RegionOrder StateOrder StatusOrder ZipcodeProduct Card IdProduct Category IdProduct DescriptionProduct ImageProduct NameProduct PriceProduct Statusshipping date (DateOrders)Shipping Mode
180509PAYMENT340.000000335.980011Advance shipping045FishingCaguasPuerto RicoXXXXXXXXXMelissa7WilcoxXXXXXXXXXCorporatePR9453 High Concession725.07Fan Shop18.359095-66.079956Pacific AsiaGuangshuiChina71/16/2016 6:4926052100464.00.1665202399.9800110.001399.980011335.9800110.000000Eastern AsiaHubeiPENDING_PAYMENTNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 6:49Standard Class
180510PAYMENT34165.990005331.980011Advance shipping045FishingCaguasPuerto RicoXXXXXXXXXMelissa7WilcoxXXXXXXXXXCorporatePR9453 High Concession725.07Fan Shop18.359095-66.079956Pacific AsiaGuangshuiChina71/16/2016 6:4926052100468.00.1765201399.9800110.501399.980011331.980011165.990005Eastern AsiaHubeiPENDING_PAYMENTNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 6:49Standard Class
180511DEBIT22157.429993327.980011Shipping on time045FishingChula VistaEE. UU.XXXXXXXXXOlivia9314SmithXXXXXXXXXConsumerCA3760 Stony Promenade91911.07Fan Shop32.611141-117.073662Pacific AsiaChengduChina93141/16/2016 6:2826051100472.00.1865195399.9800110.481399.980011327.980011157.429993Eastern AsiaSichuanON_HOLDNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/18/2016 6:28Second Class
180512DEBIT6486.400002319.980011Late delivery145FishingCaguasPuerto RicoXXXXXXXXXMary7396MaddenXXXXXXXXXHome OfficePR9918 Lazy Cape725.07Fan Shop18.245256-66.370621Pacific AsiaChengduChina73961/16/2016 6:0726050100480.00.2065194399.9800110.271399.980011319.98001186.400002Eastern AsiaSichuanCOMPLETENaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/22/2016 6:07Standard Class
180513PAYMENT34119.989998299.989990Advance shipping045FishingLancasterEE. UU.XXXXXXXXXMary3080SmithXXXXXXXXXHome OfficeOH8600 Red Goose Abbey43130.07Fan Shop39.715977-82.599297Pacific AsiaShangháiChina30801/16/2016 5:04260471004100.00.2565185399.9800110.401399.980011299.989990119.989998Eastern AsiaShangháiPENDING_PAYMENTNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 5:04Standard Class
180514CASH4440.000000399.980011Shipping on time045FishingBrooklynEE. UU.XXXXXXXXXMaria1005PetersonXXXXXXXXXHome OfficeNY1322 Broad Glade11207.07Fan Shop40.640930-73.942711Pacific AsiaShangháiChina10051/16/2016 3:402604310040.00.0065177399.9800110.101399.980011399.98001140.000000Eastern AsiaShangháiCLOSEDNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/20/2016 3:40Standard Class
180515DEBIT32-613.770019395.980011Late delivery145FishingBakersfieldEE. UU.XXXXXXXXXRonald9141ClarkXXXXXXXXXCorporateCA7330 Broad Apple Moor93304.07Fan Shop35.362545-119.018700Pacific AsiaHirakataJapón91411/16/2016 1:342603710044.00.0165161399.980011-1.551399.980011395.980011-613.770019Eastern AsiaOsakaCOMPLETENaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 1:34Second Class
180516TRANSFER54141.110001391.980011Late delivery145FishingBristolEE. UU.XXXXXXXXXJohn291SmithXXXXXXXXXCorporateCT97 Burning Landing6010.07Fan Shop41.629959-72.967155Pacific AsiaAdelaideAustralia2911/15/2016 21:002602410048.00.0265129399.9800110.361399.980011391.980011141.110001OceaniaAustralia del SurPENDINGNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/20/2016 21:00Standard Class
180517PAYMENT34186.229996387.980011Advance shipping045FishingCaguasPuerto RicoXXXXXXXXXMary2813SmithXXXXXXXXXConsumerPR2585 Silent Autumn Landing725.07Fan Shop18.213350-66.370575Pacific AsiaAdelaideAustralia28131/15/2016 20:1826022100412.00.0365126399.9800110.481399.980011387.980011186.229996OceaniaAustralia del SurPENDING_PAYMENTNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/18/2016 20:18Standard Class
180518PAYMENT44168.949997383.980011Shipping on time045FishingCaguasPuerto RicoXXXXXXXXXAndrea7547OrtegaXXXXXXXXXConsumerPR697 Little Meadow725.07Fan Shop18.290380-66.370613Pacific AsiaNagercoilIndia75471/15/2016 18:5426018100416.00.0465113399.9800110.441399.980011383.980011168.949997South AsiaTamil NaduPENDING_PAYMENTNaN100445NaNhttp://images.acmesports.sports/Field+%26+Stream+Sportsman+16+Gun+Fire+SafeField & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 18:54Standard Class